Patent Publication Number: US-2023136952-A1

Title: System and methods for an immediate shutdown of charging for an electric aircraft

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of Non-provisional application Ser. No. 17/515,448 filed on Oct. 30, 2021 and entitled “SYSTEM AND METHODS FOR AN IMMEDIATE SHUTDOWN OF AN ELECTRIC VEHICLE CHARGER,” 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 vehicles. In particular, the present invention is directed to a system and methods for an immediate shutdown of charging for an electric vehicle. 
     BACKGROUND 
     Electric vehicles allow for a quiet and efficient experience, while not requiring fossil fuels. As infrastructure around charging electric vehicles grows, it is critical to ensure the proper operation of electric vehicle chargers for safety purposes. 
     SUMMARY OF THE DISCLOSURE 
     In an aspect, a system for an immediate shutdown of charging for an electric vehicle may include a sensor communicatively connected to an electric vehicle charging connection between a charger and an electric vehicle, wherein the sensor is configured to: identify a communication of the charging connection; detect a charging characteristic of the communication, and generate a charging datum based on the charging characteristic; and a control circuit communicatively connected to: the sensor; the control circuit configured to receive the charging datum of the sensor; determine a disruption element as a function of the charging datum; and disable the charging connection based on the disruption element. 
     In another aspect, a method for an immediate shutdown of an charging for an electric vehicle may include: identifying, by a sensor communicatively connected to an electric vehicle charging connection, a communication between a charger and an electric vehicle; detecting, by the sensor, a charging characteristic of the communication; generating, by the sensor, a charging datum based on the charging characteristic; receiving, by a control circuit communicatively connected to the sensor, the charging datum of the sensor; determining, by the control circuit, a disruption element as a function of the charging datum; and disabling, by the control circuit, the charger connection based on the disruption element. 
     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 block diagram of an exemplary embodiment of a system for an immediate shutdown of charging for electric vehicle in accordance with aspects of the invention thereof; 
         FIG.  2    is a diagrammatic representation of an exemplary embodiment of a sensor suite in accordance with aspects of the invention thereof; 
         FIG.  3    is a flow diagram illustrating an exemplary method of a method for an immediate shutdown of charging for an electric vehicle charger in accordance with aspects of the invention thereof; 
         FIG.  4    is a diagrammatic representation illustrating an isometric view of an electric aircraft in accordance with aspects of the invention thereof; 
         FIG.  5    is a block diagram illustrating an exemplary machine-learning module that can be used to implement any one or more of the methodologies disclosed in this disclosure and any one or more portions thereof in accordance with aspects of the invention thereof; and 
         FIG.  6    is a block diagram of a computing device in accordance with aspects of the invention thereof. 
     
    
    
     The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted. 
     DETAILED DESCRIPTION 
     At a high level, aspects of the present disclosure are directed to a system and methods for an immediate shutdown of charging for an electric vehicle. More specifically, aspects of the present disclosure can be used to monitor and provide safety measures during the process of charging an electric vehicle. In an embodiment, aspects relate specifically to a sensor communicatively connected to an electric vehicle charging connection between a charger and an electric vehicle. The sensor may transmit detected charging datum to a control circuit, which may shutdown the charging connection if a disruption element is determined as a function of the charging datum. connector for interfacing with an electric vehicle for recharging. An immediate shutdown of an charging the electric vehicle may prevent catastrophic failure of and/or damage to a the batteries of an electric vehicle and/or charger. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples. 
     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. For purposes of description herein, the terms “upper”, “lower”, “left”, “rear”, “right”, “front”, “vertical”, “horizontal”, and derivatives thereof shall relate to orientations as illustrated for exemplary purposes in  FIG.  4   . Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification, are simply embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise. 
     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. 
     Referring now to  FIG.  1   , a system  100  for an immediate shutdown of charging for an electric vehicle  116  is illustrated in accordance with one or more embodiments of the present disclosure. In one or more embodiments, system  100  includes a sensor  108  communicatively connected to an electric vehicle charging connection  112  between an electric vehicle charger  104  (also referred to herein as a “charger”) and an electric vehicle  116 . In one or more embodiments, sensor  108  is configured to identify a communication of electric vehicle charging connection  112  (also referred to herein as a “charging connection”) between charger  104  and electric vehicle  116 . For instance, and without limitation, sensor  108  may recognize that a charging connection has been created between charger  104  and electric vehicle  116  that facilitates communication between charger  104  and electric vehicle  116 . For example, and without limitation, sensor  108  may identify a change in current through electric vehicle port, indicating connector is in electric communication with, for example, a charger  104 , as discussed further below. For the purposes of this disclosure, a “charging connection” is a connection associated with charging a power source, such as, for example, a battery. Charging connection  112  may be a wired or wireless connection, as discussed further below in this disclosure. Charging connection  112  may include a communication between charger  104  and electric vehicle  116 . For example, and without limitation, one or more communications between charger  104  and electric vehicle  116  may be facilitated by charging connection  112 . As used in this disclosure, “communication” is an attribute where two or more relata interact with one another, for example, within a specific domain or in a certain manner. In some cases, communication between two or more relata may be of a specific domain, such as, and without limitation, electric communication, fluidic communication, informatic communication, mechanic communication, and the like. As used in this disclosure, “electric communication” is an attribute wherein two or more relata interact with one another by way of an electric current or electricity in general. For example, and without limitation, a communication between charger  104  and electric vehicle  116  may include an electric communication. As used in this disclosure, a “fluidic communication” is an attribute wherein two or more relata interact with one another by way of a fluidic flow or fluid in general. For example, and without limitation, a coolant may flow between charger  104  and electric vehicle  116  when there is a charging connection between charger  104  and electric vehicle  116 . As used in this disclosure, “informatic communication” is an attribute wherein two or more relata interact with one another by way of an information flow or information in general. As used in this disclosure, “mechanic communication” is an attribute wherein two or more relata interact with one another by way of mechanical means, for instance mechanic effort (e.g., force) and flow (e.g., velocity). 
     In one or more embodiments, communication of charging connection  112  may include various forms of communication. For example, and without limitation, an electrical contact without making physical contact, for example, by way of inductance, may be made between charger  104  and electric vehicle  116  to facilitate communication. Exemplary conductor materials include metals, such as without limitation copper, nickel, steel, and the like. In one or more embodiments, a contact of charger  104  may be configured to provide electrical communication with a mating component within a port of electric vehicle  116 . In one or more embodiments, contact may be configured to mate with an external connector. As used in this disclosure, a “connector” is a distal end of a tether or a bundle of tethers, e.g., hose, tubing, cables, wires, and the like, which is configured to removably attach with a mating component, for example without limitation a port. As used in this disclosure, a “port” is an interface for example of an interface configured to receive another component or an interface configured to transmit and/or receive signal on a computing device. For example, in the case of an electric vehicle port, the port interfaces with a number of conductors and/or a coolant flow path by way of receiving a connector. In the case of a computing device port, the port may provide an interface between a signal and a computing device. A connector may include a male component having a penetrative form and port may include a female component having a receptive form, receptive to the male component. Alternatively or additionally, connector may have a female component and port may have a male component. In some cases, connector may include multiple connections, which may make contact and/or communicate with associated mating components within port, when the connector is mated with the port. 
     With continued reference to  FIG.  1   , sensor  108  may include one or more sensors. As used in this disclosure, a “sensor” is a device that is configured to detect an input and/or a phenomenon and transmit information related to the detection. For example, and without limitation, a sensor may transduce a detected charging phenomenon and/or characteristic, such as, and without limitation, temperature, voltage, current, pressure, and the like, into a sensed signal. Sensor  108  may detect a plurality of data about charging connection  112 , electric vehicle  116 , and/or charger  104 . A plurality of data about, for example, charging connection  112  may include, but is not limited to, battery quality, battery life cycle, remaining battery capacity, current, voltage, pressure, temperature, moisture level, and the like. In one or more embodiments, and without limitation, sensor  108  may include a plurality of sensors. In one or more embodiments, and without limitation, sensor  108  may include one or more temperature sensors, voltmeters, current sensors, hydrometers, infrared sensors, photoelectric sensors, ionization smoke sensors, motion sensors, pressure sensors, radiation sensors, level sensors, imaging devices, moisture sensors, gas and chemical sensors, flame sensors, electrical sensors, imaging sensors, force sensors, Hall sensors, and the like. Sensor  108  may be a contact or a non-contact sensor. For instance, and without limitation, sensor  108  may be connected to electric vehicle  116 , charger  104 , and/or a control circuit  120 . In other embodiments, sensor  108  may be remote to electric vehicle  116 , charger  104 , and/or control circuit  120 . As discussed further in this disclosure below, control circuit  120  may include a computing device, a processor, a pilot control, a controller, such as a flight controller, and the like. In one or more embodiments, sensor  108  may transmit/receive signals to/from control circuit  120 . Signals may include electrical, electromagnetic, visual, audio, radio waves, or another undisclosed signal type alone or in combination. 
     Sensor  108  may include a plurality of independent sensors, where any number of the described sensors may be used to detect any number of physical or electrical quantities associated with communication of charging connection  112 . Independent sensors may include separate sensors measuring physical or electrical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as a user graphical interface. In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability of sensor  108  to detect phenomenon may be maintained. 
     Still referring to  FIG.  1   , sensor  108  may include a motion sensor. A “motion sensor”, for the purposes of this disclosure, refers to a device or component configured to detect physical movement of an object or grouping of objects. One of ordinary skill in the art would appreciate, after reviewing the entirety of this disclosure, that motion may include a plurality of types including but not limited to: spinning, rotating, oscillating, gyrating, jumping, sliding, reciprocating, or the like. Sensor  108  may include, torque sensor, gyroscope, accelerometer, torque sensor, magnetometer, inertial measurement unit (IMU), pressure sensor, force sensor, proximity sensor, displacement sensor, vibration sensor, among others. 
     In some embodiments, sensor  108  may include a pressure sensor. A “pressure”, for the purposes of this disclosure, and as would be appreciated by someone of ordinary skill in the art, is a measure of force required to stop a fluid from expanding and is usually stated in terms of force per unit area. In non-limiting exemplary embodiments, a pressure sensor may be configured to measure an atmospheric pressure and/or a change of atmospheric pressure. In some embodiments, a pressure sensor may include an absolute pressure sensor, a gauge pressure sensor, a vacuum pressure sensor, a differential pressure sensor, a sealed pressure sensor, and/or other unknown pressure sensors or alone or in a combination thereof. The pressor sensor may include a barometer. In some embodiments, the pressure sensor may be used to indirectly measure fluid flow, speed, water level, and altitude. In some embodiments, a pressure sensor may be configured to transform a pressure into an analogue electrical signal. In some embodiments, the pressure sensor may be configured to transform a pressure into a digital signal. 
     In one or more embodiments, sensor  108  may include a moisture sensor. “Moisture”, as used in this disclosure, is the presence of water, which may include vaporized water in air, condensation on the surfaces of objects, or concentrations of liquid water. Moisture may include humidity. “Humidity”, as used in this disclosure, is the property of a gaseous medium (almost always air) to hold water in the form of vapor. 
     In one or more embodiments, sensor  108  may include electrical sensors. Electrical sensors may be configured to measure voltage across a component, electrical current through a component, and resistance of a component. In one or more embodiments, sensor  108  may include thermocouples, thermistors, thermometers, infrared sensors, resistance temperature sensors (RTDs), semiconductor based integrated circuits (ICs), a combination thereof, or another undisclosed sensor type, alone or in combination. Temperature, for the purposes of this disclosure, and as would be appreciated by someone of ordinary skill in the art, is a measure of the heat energy of a system. Temperature, as measured by any number or combinations of sensors present within sensor  108 , may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin (° K), or another scale alone or in combination. The temperature measured by sensors may comprise electrical signals, which are transmitted to their appropriate destination wireless or through a wired connection. 
     In some embodiments, sensor  108  may include a plurality of sensing devices, such as, but not limited to, temperature sensors, humidity sensors, accelerometers, electrochemical sensors, gyroscopes, magnetometers, inertial measurement unit (IMU), pressure sensor, proximity sensor, displacement sensor, force sensor, vibration sensor, air detectors, hydrogen gas detectors, and the like. Sensor  108  may be configured to detect a plurality of data, as discussed further below in this disclosure. A plurality of data may be detected from charger  104 , charging connection  112 , and/or electric vehicle  116  via a communication of charging connection  112 . 
     In some embodiments, a plurality of data may be detected from an environment of electric vehicle  116 . A plurality of data may include, but is not limited to, airborne particles, weather, temperature, air quality, and the like. In some embodiments, airborne particles may include hydrogen gas and/or any gas that may degrade a battery of electric vehicle  116 . Sensor  108  may detect a plurality of data about a power source  124  of electric vehicle  116 . 
     In one or more embodiments, sensor  108  may include a sensor suite which may include a plurality of sensors that may detect similar or unique phenomena, as discussed further in  FIG.  2   . For example, in a non-limiting embodiment, a sensor suite may include a plurality of voltmeters or a mixture of voltmeters and thermocouples. System  100  may include a plurality of sensors in the form of individual sensors or a sensor suite working in tandem or individually. A sensor suite may include a plurality of independent sensors, as described in this disclosure, where any number of the described sensors may be used to detect any number of physical or electrical quantities associated with a charging connection. Independent sensors may include separate sensors measuring physical or electrical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as control circuit  120 . In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability to detect phenomenon is maintained. 
     In one or more embodiments, sensor  108  may include a sense board. A sense board may have at least a portion of a circuit board that includes one or more sensors configured to, for example, measure a temperature of power source  124  of electric aircraft  116  and/or power source  128  of charger  104 . In one or more embodiments, a sense board may be connected to one or more battery modules or cells of a power source. In one or more embodiments, a sense board may include one or more circuits and/or circuit elements, including, for example, a printed circuit board component. A sense board may include, without limitation, a control circuit configured to perform and/or direct any actions performed by the sense board and/or any other component and/or element described in this disclosure. The control circuit may include any analog or digital control circuit, including without limitation a combinational and/or synchronous logic circuit, a processor, microprocessor, microcontroller, or the like. 
     Still referring to  FIG.  1   , sensor  108  is configured to detect a charging characteristic  132  of a communication. As used in this disclosure, a “sensor” is a device that is configured to detect an input and/or a phenomenon and transmit information related to the detection. For example, and without limitation, a sensor may transduce a detected phenomenon, such charging characteristic  132 . As used in this disclosure, a “charging characteristic” is a detectable phenomenon associated with charging a power source. In one or more embodiments, a charging characteristic includes temperature, voltage, current, pressure, moisture, and the like. In one or more embodiments, sensor  108  may be configured to detect charging characteristic  132  of a communication between charger  104  and electric vehicle  116  and then transmit a sensor output signal representative of charging characteristic  132 , where the sensor signal includes a charging datum  136 . As used in this disclosure, a “sensor signal” is a representation of a charging characteristic  132  that sensor  108  may generate. Sensor signal may include charging datum  136 . For instance, and without limitation, sensor  108  is configured to generate charging datum  136  of a communication. For the purposes of this disclosure, a “charging datum” is an electronic signal representing a quantifiable element of data correlated to a charging characteristic. For example, and without limitation, power source  124  of electric vehicle  116  may need to be a certain temperature to operate properly; charging datum  136  may provide a numerical value, such as a temperature in degrees, that indicates the current temperature of a charging power source. For example, and without limitation, sensor  108  may be a temperature sensor that detects the temperature of a power source of electric vehicle  116  to be at a numerical value of 100° F. and transmits the corresponding charging datum to, for example, control circuit  120 . In another example, and without limitation, sensor  108  may be a current sensor and a voltage sensor that detects a current value and a voltage value, respectively, of a power source of an electric vehicle. Such charging datum may be associated with an operating condition of power sources  124 , 128  such as, for example, a state of charge (SoC) or a depth of discharge (DoD) of the power source. For example, and without limitation, charging datum  136  may include, for example, a temperature, a state of charge, a moisture level, a state of health (or depth of discharge), or the like. A sensor signal may include any signal form described in this disclosure, for example digital, analog, optical, electrical, fluidic, and the like. In some cases, a sensor, a circuit, and/or a controller may perform one or more signal processing steps on a signal. For instance, sensor, circuit, and/or controller may analyze, modify, and/or synthesize a signal in order to improve the signal, for instance by improving transmission, storage efficiency, or signal to noise ratio. 
     Exemplary methods of signal processing may include analog, continuous time, discrete, digital, nonlinear, and statistical. Analog signal processing may be performed on non-digitized or analog signals. Exemplary analog processes may include passive filters, active filters, additive mixers, integrators, delay lines, compandors, multipliers, voltage-controlled filters, voltage-controlled oscillators, and phase-locked loops. Continuous-time signal processing may be used, in some cases, to process signals which varying continuously within a domain, for instance time. Exemplary non-limiting continuous time processes may include time domain processing, frequency domain processing (Fourier transform), and complex frequency domain processing. Discrete time signal processing may be used when a signal is sampled non-continuously or at discrete time intervals (i.e., quantized in time). Analog discrete-time signal processing may process a signal using the following exemplary circuits sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. Digital signal processing may be used to process digitized discrete-time sampled signals. Commonly, digital signal processing may be performed by a computing device or other specialized digital circuits, such as without limitation an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a specialized digital signal processor (DSP). Digital signal processing may be used to perform any combination of typical arithmetical operations, including fixed-point and floating-point, real-valued and complex-valued, multiplication and addition. Digital signal processing may additionally operate circular buffers and lookup tables. Further non-limiting examples of algorithms that may be performed according to digital signal processing techniques include fast Fourier transform (FFT), finite impulse response (FIR) filter, infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters. Statistical signal processing may be used to process a signal as a random function (i.e., a stochastic process), utilizing statistical properties. For instance, in some embodiments, a signal may be modeled with a probability distribution indicating noise, which then may be used to reduce noise in a processed signal. 
     In one or more embodiments, sensor  108  may include sensors configured to measure charging characteristics  132 , such as physical and/or electrical parameters related to charging connection  112 . For example, and without limitation, sensor  108  may measure temperature and/or voltage, of battery modules and/or cells of a power source of electric vehicle  116  and/or charger  104 . Sensor  108  may be configured to detect failure within each battery module, for instance and without limitation, as a function of and/or using detected charging characteristics. In one or more exemplary embodiments, battery cell failure may be characterized by a spike in temperature; sensor  108  may be configured to detect that increase in temperature and generate a corresponding signal, such as charging datum  136  of the communication. In other exemplary embodiments, sensor  108  may detect voltage and direct the charging of individual battery cells according to charge level. Detection may be performed using any suitable component, set of components, and/or mechanism for direct or indirect measurement and/or detection of voltage levels, including without limitation comparators, analog to digital converters, any form of voltmeter, or the like. 
     Still referring to  FIG.  1   , control circuit  120  is configured to receive charging datum  136  from sensor  108 . Control circuit  120  may receive charging datum via a wired or wireless communication between control circuit  120  and sensor  108 . In one or more embodiments, control circuit  120  is configured to determine a disruption element as a function of the received charging datum  136 . For purposes of this disclosure, a “disruption element” is an element of information regarding a present-time failure, fault, or degradation of a condition or working order of a charging connection. In one or more embodiments, disruption element  140  may be determined as a function of charging datum  136 , as discussed further in this disclosure. 
     In one or more embodiments, outputs, such as charging datum  136 , from sensor  108  or any other component present within system  100  may be analog or digital. Onboard or remotely located processors can convert those output signals from sensor  108  or sensor suite to a usable form by the destination of those signals, such as control circuit  120 . The usable form of output signals from sensors, through processor may be either digital, analog, a combination thereof, or an otherwise unstated form. Processing may be configured to trim, offset, or otherwise compensate the outputs of sensor suite. Based on sensor output, the processor can determine the output to send to downstream component. Processor can include signal amplification, operational amplifier (Op-Amp), filter, digital/analog conversion, linearization circuit, current-voltage change circuits, resistance change circuits such as Wheatstone Bridge, an error compensator circuit, a combination thereof or otherwise undisclosed components. In some embodiments sensor  108  may be configured to communicate charging datum  136 , for instance, by way of network. Exemplary charging datum may include charging characteristics, for example, represented by way of at least a sensor signal. In some cases, charging datum may include one or more of a state of charge of a power source, a temperature of a power source, any other metric associated with power source health, temperature of ambient air, cost of electricity consumed, and the like. 
     Still referring to  FIG.  1   , control circuit  120  is configured to disable charging connection  112  based on disruption element  140 . In one or more embodiments, if an immediate shutdown via a disablement of charging connection  112  is initiated, then control circuit  120  may also generate a signal to notify users, support personnel, safety personnel, flight crew, maintainers, operators, emergency personnel, aircraft computers, or a combination thereof. System  100  may include a display. A display may be coupled to electric vehicle  116 , charger  104 , or a remote device. A display may be configured to show a disruption element to a user. In one or more embodiments, control circuit  120  may be configured to disable charging connection  112  based on disruption element  140 . For instance, and without limitation, control circuit  120  may be configured to detect a charge reduction event, defined for purposes of this disclosure as any temporary or permanent state of a battery cell requiring reduction or cessation of charging. A charge reduction event may include a cell being fully charged and/or a cell undergoing a physical and/or electrical process that makes continued charging at a current voltage and/or current level inadvisable due to a risk that the cell will be damaged, will overheat, or the like. Detection of a charge reduction event may include detection of a temperature of the cell above a preconfigured threshold, detection of a voltage and/or resistance level above or below a preconfigured threshold, or the like. 
     In one or more embodiments, disruption element  140  may indicate a power source  124 , 128  of electric aircraft  116  and/or charger  104 , respectively, is operating outside of an acceptable operation condition represented by a preconfigured threshold (also referred to herein as a “threshold”). For the purposes of this disclosure, a “threshold” is a set desired range and/or value that, if exceeded by a value of charging datum, initiates a specific reaction of control circuit  120 . A specific reaction may be, for example, a disablement command  144 , which is discussed further below in this disclosure. Threshold may be set by, for example, a user or control circuit based on, for example, prior use or an input. In one or more embodiments, if charging datum  136  is determined to be outside of a threshold, disruption element  140  is determined by control circuit  120  and disablement command  144  is generated. For example, and without limitation, charging datum  136  may indicate that a power source  124  of electric vehicle  116  and/or power source  128  of charger  104  has a temperature of 100° F. Such a temperature may be outside of a preconfigured threshold of, for example, 75° F. of an operational condition, such as temperature, of a power source and thus charging connection  112  may be disabled by control circuit  120  to prevent overheating of or permanent damage to power source  124 , 128 . For the purposes of this disclosure, a “disablement command” is a signal transmitted to an electric vehicle and/or a charger providing instructions and/or a command to disable and/or terminate a charging connection between an electric vehicle and a charger. Disabling charging connection  112  may include terminating a communication between electric vehicle  116  and charger  104 . For example, and without limitation, disabling charging connection  112  may include terminating a power supply to charger  104  so that charger  104  is no longer providing power to electrical vehicle  116 . In another example, and without limitation, disabling charging connection  112  may include electrical vehicle  116  severing the electrical connection between electrical vehicle  116  and charger  104 . In another example, and without limitation, disabling charging connection  112  may include terminating a power supply to electric vehicle  116 . In another example, and without limitation, disabling charging connection  112  may include using a relay or switch between charger  112  and vehicle  116  to terminate charging connection and/or a communication between charger  112  and vehicle  116 . 
     Still referring to  FIG.  1   , as previously mentioned in this disclosure, system  100  may include control circuit  120 . In one or more exemplary embodiments, control circuit may include a computing device. A computing device may include any computing device as described in this disclosure, including without limitation a microcontroller, processor, microprocessor, flight controller, digital signal processor (DSP), and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 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 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 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 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 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 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 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.  1   , computing device 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 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 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.  1   , in some cases, control circuit  120  is configured to receive charging datum  136  of sensor  108 . As previously mentioned, sensor  108  detects a charging characteristic  132  of a communication of charging connection  112 . A corresponding sensor signal that includes charging datum  136  is then generated and transmitted by sensor  108  to control circuit  120 . Once control circuit  120  receives charging datum  136 , control circuit  120  determines a disruption element  140  as a function of the charging datum  136 . For instance, and without limitation, a disruption element may include a charging failure of electric vehicle  116 . For example, and without limitation, sensor  108  may detect an amount of current so high that control circuit  120  determines that a charging failure as a function of the received charging datum  136 . In one or more embodiments, control circuit  120  is configured to disable charging connection  112  based on determined disruption element  140 . For example, and without limitation, after determining a disruption element, control circuit  120  may generate a control signal, such as disablement command  144 , providing instructions to an electric vehicle  116 , charger  104 , a relay or switch of charging connection  112 , or the like, to disable a communication and/or charging connection  112 . In another example, control circuit  120  may directly disable charging connection  112 . In one or more embodiments, control circuit  120  is configured to stop charging by terminating an electrical communication between electric vehicle  116  and charger  104 . 
     Still referring to  FIG.  1   , charger  104  may include power source  128 , which may supply electrical energy to power source  124  of electric vehicle  116 . As used in this disclosure, a “charger” is an electrical system and/or circuit that increases electrical energy in an energy store, for example a battery. In one or more embodiments, charger  104  includes a charging component that is configured to supply power to electric vehicle  116 . For example, and without limitation, charger  104  may supply power to power source  124  of electric vehicle  116 . For example, and without limitation, charger  104  may be configured to charge and/or recharge a plurality of electric aircrafts at a time. As used in this disclosure, “charging” is a process of flowing electrical charge in order to increase stored energy within a power source. In one or more non-limiting exemplary embodiments, a power source includes a battery and charging includes providing an electrical current to the battery. In some embodiments, charger  104  may be constructed from any of variety of suitable materials or any combination thereof. In some embodiments, charger  104  may be constructed from metal, concrete, polymers, or other durable materials. In one or more embodiments, charger  104  may be constructed from a lightweight metal alloy. In some embodiments, charger  104  may be included a charging pad. The charging pad may include a landing pad, where the landing pad may be any designated area for the electric vehicle to land and/or takeoff. In one or more embodiments, landing pad may be made of any suitable material and may be any dimension. In some embodiments, landing pad may be a helideck or a helipad. In one or more embodiments, charger  104  may be in electric communication with a power converter and power source, such as a battery of electric vehicle  116 . In some cases, charger  104  may be configured to charge power source  124  with an electric current from a power converter. In some cases, charger  104  may include one or electrical components configured to control flow of an electrical recharging current, such as without limitation switches, relays, direct current to direct current (DC-DC) converters, and the like. In some case, charger  104  may include one or more circuits configured to provide a variable current source to provide electrical charging current, for example an active current source. Non-limiting examples of active current sources include active current sources without negative feedback, such as current-stable nonlinear implementation circuits, following voltage implementation circuits, voltage compensation implementation circuits, and current compensation implementation circuits, and current sources with negative feedback, including simple transistor current sources, such as constant currant diodes, Zener diode current source circuits, LED current source circuits, transistor current, and the like, Op-amp current source circuits, voltage regulator circuits, and curpistor tubes, to name a few. In some cases, one or more circuits within charger  104  or within communication with charger  104  are configured to affect electrical recharging current according to control signal from, for example, a controller. For instance, and without limitation, a controller may control at least a parameter of the electrical charging current. For example, in some cases, controller may control one or more of current (Amps), potential (Volts), and/or power (Watts) of electrical charging current by way of control signal. In some cases, controller may be configured to selectively engage electrical charging current, for example ON or OFF by way of control signal. In one or more embodiments, disablement command  144  from control circuit  120  may be received by controller, which, in response, may, for example, terminate power to charger  104 . 
     Still referring to  FIG.  1   , for the purposes of this disclosure, a “power source” is a device and/or component used to store and provide electrical energy to, for example, an electric vehicle or another power source. For example, and without limitation, power sources  124 , 128  may be a battery and/or a battery pack having one or more battery modules or battery cells. In some cases, power source  128  may include a charging battery (i.e. a battery used for charging other batteries). A charging battery is notably contrasted with an electric vehicle battery, which is located, for example, upon an electric aircraft. As used in this disclosure, an “electrical charging current” is a flow of electrical charge that facilitates an increase in stored electrical energy of an energy storage, such as without limitation a power source. Power source  128  may include a plurality of batteries, battery modules, and/or battery cells. Power source  128  may be configured to store a range of electrical energy, for example, a range of between about 5 KWh and about 5,000 KWh. Power source  128  may house a variety of electrical components. In one embodiment, power source  128  may contain a solar inverter. Solar inverter may be configured to produce on-site power generation. In one embodiment, power generated from solar inverter may be stored in a power source. In some embodiments, power source  128  may include a used electric vehicle battery no longer fit for service in a vehicle. In some embodiments, power source  128  may include any component with the capability of recharging a power source  124  of electric vehicle  116 . In some embodiments, power source  128  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 one or more embodiments, power sources  124 , 128  may be one or more various types of batteries, such as a pouch cell battery, stack batteries, prismatic battery, lithium-ion cells, or the like. In one or more embodiments, power sources  124 , 128  may include a battery, flywheel, rechargeable battery, flow battery, glass battery, lithium-ion battery, ultra battery, and the like thereof. In one or more embodiments, power sources  124 , 128  may have high power density where electrical power and power source can usefully produce per unit of volume and/or mass is relatively high. As used in this disclosure, “electrical power” is a rate of electrical energy per unit time. A power source may include a device for which power that may be produced per unit of volume and/or mass has been optimized, for instance at an expense of maximal total specific energy density or power capacity. In one or more non-limiting exemplary embodiments, power source may include batteries used for starting applications including Li ion batteries which may include NCA, NMC, Lithium iron phosphate (LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may be mixed with another cathode chemistry to provide more specific power if the application requires Li metal batteries, which have a lithium metal anode that provides high power on demand, Li ion batteries that have a silicon or titanite anode, energy source may be used, in an embodiment, to provide electrical power to an electric aircraft or drone, such as an electric aircraft vehicle, during moments requiring high rates of power output, including without limitation takeoff, landing, thermal de-icing and situations requiring greater power output for reasons of stability, such as high turbulence situations, as described in further detail below. A power source may include, without limitation a battery using nickel based chemistries such as nickel cadmium or nickel metal hydride, a battery using lithium ion battery chemistries such as a nickel cobalt aluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide (LMO), a battery using lithium polymer technology, lead-based batteries such as without limitation lead acid batteries, metal-air batteries, or any other suitable battery. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various devices of components that may be used as a power source. 
     In one or more embodiments, each power source  124 , 128  may include a plurality of power sources. For example, and without limitation, power source may include batteries connected in parallel or in series or a plurality of modules connected either in series or in parallel designed to satisfy both power and energy requirements. Connecting batteries in series may increase a potential of at least an energy source which may provide more power on demand. High potential batteries may require cell matching when high peak load is needed. As more cells are connected in strings, there may exist a possibility of one cell failing which may increase resistance in module and reduce overall power output as voltage of the module may decrease as a result of that failing cell. Connecting batteries in parallel may increase total current capacity by decreasing total resistance, and it also may increase overall amp-hour capacity. Overall energy and power outputs of at least a power source may be based on individual battery cell performance or an extrapolation based on a measurement of at least an electrical parameter. In an embodiment where power source includes a plurality of battery cells, overall power output capacity may be dependent on electrical parameters of each individual cell. If one cell experiences high self-discharge during demand, power drawn from at least a power source may be decreased to avoid damage to a weakest cell. Energy source may further include, without limitation, wiring, conduit, housing, cooling system and battery management system. Persons skilled in the art will be aware, after reviewing the entirety of this disclosure, of many different components of an energy source. Exemplary power sources are disclosed in detail in U.S. patent application Ser. No. 16/948,157 and Ser. No. 16/048,140 both entitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE”, which are incorporated in their entirety herein by reference. 
     In one or more embodiments, control circuit  120  may be configured to control one or more electrical charging current within a conductor and/or coolant flow within a hose of charger  104 . In other embodiments, control circuit  120  may be configured to control electrical charging and/or coolant flow within the electric vehicle port of electrical vehicle  116 . In one or more embodiments, control circuit  120  may be a controller. As used in this disclosure, a “controller” is a logic circuit, such as an application-specific integrated circuit (ASIC), FPGA, comparator, Op-amp current source circuit, microcontroller, computing device, any combination thereof, and the like, that is configured to control a system and/or subsystem. For example, controller may be configured to control a coolant source  148 , a ventilation component  152 , power source  128 , or any other charger component. In some embodiments, controller may control coolant source  148  and/or charger power source  128  according to disablement command  144 . In some embodiments, disablement command  144  may be analog. In some cases, disablement command  144  may be digital. In one or more embodiments, disablement command  144  may be communicated according to one or more communication protocols, for example without limitation Ethernet, universal asynchronous receiver-transmitter, and the like. In some cases, disablement command  144  may be a serial signal. In some cases, disablement command may be a parallel signal. Disablement command  144  may be communicated by way of a network, for example a controller area network (CAN). In some cases, disablement command  144  may include commands to operate one or more of coolant sources  148 , ventilation components  152 , and/or charger power sources  128 . For example, and without limitation, coolant source  148  may include a valve to control coolant flow and control circuit  120  may be configured to control the valve by way of disablement command  144 . In some cases, coolant source  148  may include a flow source (e.g., a pump, a fan, or the like) and control circuit  120  may be configured to control the flow source by way of a disablement command. For example, and without limitation, control circuit  120  may turn off a flow source of charger  104  via disablement command  144 . In another example, control circuit  120  may command electrical vehicle  116  to sever electrical connection to charger  104 . In some case, power source  104  may include one or more circuits configured to provide a variable current source to provide electric charging current, for example, an active current source. Non-limiting examples of active current sources include active current sources without negative feedback, such as current-stable nonlinear implementation circuits, following voltage implementation circuits, voltage compensation implementation circuits, and current compensation implementation circuits, and current sources with negative feedback, including simple transistor current sources, such as constant currant diodes, Zener diode current source circuits, LED current source circuits, transistor current, and the like, Op-amp current source circuits, voltage regulator circuits, and curpistor tubes, to name a few. In one or more embodiments, one or more circuits within charger  104  or within electric vehicle  116 . are configured to affect electrical charging current according to disruption element  140  from control circuit  120 , such that control circuit  120  may control at least a parameter of the electrical charging current, such as an ON and OFF of circuits. For instance, and without limitation, control circuit  120  may control one or more of current (Amps), potential (Volts), and/or power (Watts) of electrical charging current by way of disruption command  144 . For example, control circuit  120  may be configured to selectively engage electrical charging current, for example, ON or OFF by way of disruption command  144 . In one or more embodiments, control circuit  120  is configured to provide protection to prevent damage to electric vehicle  116 , charger  104 , and/or injury to personnel by providing an immediate shutdown, such as an emergency shutdown, of charging connection  112 . For example, in some cases, control circuit  120  may be configured to start and/or stop coolant flow and/or charging current under normal and/or abnormal conditions. In some cases, control circuit  120  may include a user interface. User interface may allow personnel to interface with control circuit  120  and thereby control any system and/or subsystem of charger  104 , including but not limited to coolant source  148  and charger power source  128 . In other embodiments, user interface may allow personnel to interface with control circuit  120  and thereby control any system of electric vehicle  116 . In some cases, user interface may be configured to communicate information, such as without limitation charging data and/or disruption element to personnel. For example, and without limitation, user interface may provide indications when charger  104  or electric vehicle  116  needs servicing after control circuit  120  has transmitted disablement command  144  to disable charging connection  112  and, for example, turn off power and/or stop coolant flow. 
     With continued reference to  FIG.  1   , in some embodiments, charger  104  may include a connector configured to connect to port of electric vehicle  116  to create charging connection  112 . In such a case, connector of charger  104  may be configured to be in electric communication and/or mechanic communication with port of electric vehicle  116 . In other embodiments, charging connection  112  between charger  104  and electric vehicle  116  may be wireless, such as via induction for an electric communication or via wireless signals for an informatic communication. In other embodiments, a hose of charger  104  may be configured to be in fluidic communication with a port of electric vehicle  116 . For example, and without limitation, hose may facilitate fluidic communication between coolant source  148  and vehicle power source  124  when connector is connected to port. In one or more embodiments, coolant source  148  may pre-condition aircraft power source  124 . As used in this disclosure, “pre-conditioning” is an act of affecting a characteristic of a power source, for example power source temperature, pressure, humidity, swell, and the like, substantially prior to charging. In some cases, coolant source may be configured to pre-condition at least electric vehicle power source  124  prior to charging, by providing a coolant flow to the power source of the electric vehicle and raising and/or lowering temperature of the power source. Connector of charger  104  may include a seal configured to seal coolant. In some cases, seal may include at least one of a gasket, an O-ring, a mechanical fit (e.g., press fit or interference fit), and the like. In one or more embodiments, sensor  108  may detect a charging characteristic of seal. For example, and without limitation, if seal is leaking coolant, sensor  108  may detect a pressure charging characteristic, generate a charging datum related to the detected pressure, and transmit charging datum to control circuit  120 . Control circuit  120  may then determine a disruption element as a function of the pressure charging datum and a preconfigured pressure threshold for coolant flow. Charging datum may be determined to be outside of preconfigured threshold and thus control circuit  120  may disable charging connection as a safety measure, such as by shutting off coolant flow through hose. 
     Still referring to  FIG.  1   , sensor  108  may be configured to detect an attachment of charger  104  with an electric vehicle port, and transmit charging datum  136  to control circuit  120 . For example, and without limitation, charging datum  136  may include a signal confirming that a connector of charger  104  and a port of electric vehicle  116  have failed to properly interlock. In some embodiments, sensor  108  may include a proximity sensor that generates a proximity signal and transmits the proximity signal to control circuit  120  as a function of the charging datum. In another example, and without limitation, connector may be coupled to a proximity signal conductor. As used in this disclosure, an “proximity signal conductor” is a conductor configured to carry a proximity signal. As used in this disclosure, a “proximity signal” is a signal that is indicative of information about a location of connector. Proximity signal may be indicative of attachment of connector with a port, for instance electric vehicle port. In some cases, a proximity signal may include an analog signal, a digital signal, an electrical signal, an optical signal, a fluidic signal, or the like. In embodiments, a proximity signal conductor may be configured to conduct a proximity signal indicative of attachment between connector and an electric vehicle port. In one or more non-limiting exemplary embodiments, control circuit  120  may be configured to receive charging datum including a proximity signal from sensor  108 , which may include a proximity sensor. Proximity sensor may be electrically communicative with a proximity signal conductor. Proximity sensor may be configured to generate a proximity signal as a function of connection between connector and electric vehicle port. As used in this disclosure, a “proximity sensor” is a sensor that is configured to detect at least a phenomenon related to connecter being mated to a port. Proximity sensor may include any sensor described in this disclosure, including without limitation a switch, a capacitive sensor, a capacitive displacement sensor, a doppler effect sensor, an inductive sensor, a magnetic sensor, an optical sensor (such as without limitation a photoelectric sensor, a photocell, a laser rangefinder, a passive charge-coupled device, a passive thermal infrared sensor, and the like), a radar sensor, a reflection sensor, a sonar sensor, an ultrasonic sensor, fiber optics sensor, a Hall effect sensor, and the like. In one or more non-limiting exemplary embodiments, if control circuit  120  determines a disruption element as a function of proximity charging datum, then control circuit may disable a charging connection, such as turn off a power supply to charger  104  and thus turn off a power supply to electric vehicle  116 . 
     With continued reference to  FIG.  1   , connector may include a coolant flow path. Coolant flow path may have a distal end located substantially at connector  100 . As used in this disclosure, a “coolant flow path” is a component that is substantially impermeable to a coolant and contains and/or directs a coolant flow. As used in this disclosure, “coolant” may include any flowable heat transfer medium. Coolant may include a liquid, a gas, a solid, and/or a fluid. Coolant may include a compressible fluid and/or a non-compressible fluid. Coolant may include a non-electrically conductive liquid such as a fluorocarbon-based fluid, such as without limitation Fluorinert™ from 3M of Saint Paul, Minn., USA. In some cases, coolant may include air. As used in this disclosure, a “flow of coolant” is a stream of coolant. In some cases, coolant may include a fluid and coolant flow is a fluid flow. Alternatively or additionally, in some cases, coolant may include a solid (e.g., bulk material) and coolant flow may include motion of the solid. Exemplary forms of mechanical motion for bulk materials include fluidized flow, augers, conveyors, slumping, sliding, rolling, and the like. Coolant flow path may be in fluidic communication with a coolant source  138 . As used in this disclosure, a “coolant source” is an origin, generator, reservoir, or flow producer of coolant. In some cases, a coolant source may include a flow producer, such as a fan and/or a pump. Coolant source may include any of following non-limiting examples, air conditioner, refrigerator, heat exchanger, pump, fan, expansion valve, and the like. 
     Still referring to  FIG.  1   , in some embodiments, coolant source  148  may be further configured to transfer heat between coolant, for example coolant belonging to coolant flow, and an ambient air. As used in this disclosure, “ambient air” is air which is proximal a system and/or subsystem, for instance the air in an environment which a system and/or sub-system is operating. For example, in some cases, coolant source comprises a heart transfer device between coolant and ambient air. Exemplary heat transfer devices include, without limitation, chillers, Peltier junctions, heat pumps, refrigeration, air conditioning, expansion or throttle valves, heat exchangers (air-to-air heat exchangers, air-to-liquid heat exchangers, shell-tube heat exchangers, and the like), vapor-compression cycle system, vapor absorption cycle system, gas cycle system, Stirling engine, reverse Carnot cycle system, and the like. In some versions, computing device  104  may be further configured to control a temperature of coolant. For instance, in some cases, a sensor may be located within thermal communication with coolant, such that sensor is able to detect, measure, or otherwise quantify temperature of coolant within a certain acceptable level of precision. In some cases, sensor may include a thermometer. Exemplary thermometers include without limitation, pyrometers, infrared non-contacting thermometers, thermistors, thermocouples, and the like. In some cases, thermometer may transduce coolant temperature to a coolant temperature signal and transmit the coolant temperature signal to control circuit  120 . Control circuit  120  may receive coolant temperature charging datum and determine if there is a disruption element as a function of the coolant temperature charging datum. If control circuit  120  determines such a charging datum, control circuit may disable charging connection by, for example, turning off coolant flow through connector. Control circuit  120  may use any control method and/or algorithm used in this disclosure to control charging connection  112 , including without limitation proportional control, proportional-integral control, proportional-integral-derivative control, and the like. 
     Still referring to  FIG.  1   , charger  104  may include ventilation component  152 . Ventilation component  152  may be configured to lead a flow of air and/or airborne particles away from charger  104  and/or electric vehicle  116 . In some embodiments, ventilation component  152  may include a ventilation ducting system. A “ventilation component” as used in this disclosure is a group of holes configured to permit a flow of air away or towards an object. In some embodiments, a ventilation ducting system may be configured to direct a flow of heated air away from charger  104 . In other embodiments, a ventilation ducting system may be configured to direct a flow of cool air to charger  104 . In some embodiments, ventilation component  152  may include a plurality of exhaust devices, such as, but not limited to, vanes, blades, rotors, impellers, and the like. In some embodiments, an exhaust device of ventilation component  152  may be mechanically connected to a power source. In one or more embodiments, ventilation component  152  may have a charging connection with electric vehicle  116 . In one or more exemplary embodiments, if control circuit  120  determines a disruption element related to the communication between ventilation component  152  and vehicle  116  as a function of, for example, temperature charging datum, then control circuit may disable charging connection between ventilation component  152  and electric vehicle  116  to avoid, for example, overheating of charger  104  and/or electric vehicle if ventilation component  152  is working improperly. 
     In other embodiments, charger  104  may include, but is not limited to, an electric vehicle recharging station, a ground support cart, an electric recharging point, a charging point, a charge point, an electronic charging station, electric vehicle supply equipment, and the like. For instance, and without limitation, charger may be consistent with disclosure of electric vehicle recharging component in U.S. patent application Ser. No. 17/361,911 and titled “RECHARGING STATION FOR ELECTRIC AIRCRAFTS AND A METHOD OF ITS USE”, which is incorporated herein by reference in its entirety. In a non-limiting embodiment, charger  104  may further 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, charger  104  may be configured to deliver power stored from a power storage unit. In some embodiments, charger  104  may be configured to connect to a power storage unit through a DC-to-DC converter. In one embodiment, charger  104  may be configured to connect to a power storage unit through a DC-to-DC converter. In another embodiment, two or more electric aircrafts may be charged by charger  104 . As previously mentioned in this disclosure, charger  104  may further include a power source, such as a battery, that may further include a power supply unit. The power supply unit may be mechanically connected to charger  104 . 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, which may be configured to store, for example, 500 kwh of electrical energy. Charger  104  may house a variety of electrical components. In one embodiment, charger  104  may contain a solar inverter. A solar inverter may be configured to produce on-site power generation. In one embodiment, power generated from a solar inverter may be stored in power storage unit of charger  104 . In some embodiments, a power storage unit may include a used electric aircraft battery pack no longer fit for flight. 
     Still referring to  FIG.  1   , in one embodiment, charger  104  may include a plurality of connections to create a plurality of charging connections between charger  104  and electric vehicle  116  to comply with various electric vehicle needs. In one embodiment, charger  104  may connect to manned and unmanned electric vehicles of various sizes, such as an eVTOL or a drone. In another embodiment, charger  104  may switch between power transfer standards such as the combined charging system standard (CCS) and CHAdeMO standards. In another embodiment, charger  104  may adapt to multiple demand response interfaces. 
     Still referring to  FIG.  1   , control circuit  120  may be further configured to prevent a second communication between charger  104  and electric vehicle  116 . For example, and without limitation, if control circuit  120  determines a disruption element related to a voltage of vehicle power source  124 , the control circuit may disable, for example, an electric communication and/or mechanic communication between charger  104  and electric vehicle  116 . Control circuit  120  may then prevent, for example, a user from creating a new charging connection between the same electric vehicle or a different electric vehicle until the disruption element has been resolved and is no longer detected. For example, if a second aircraft lands on a helipad charger, the helipad charger will not create a charging connection with the second aircraft until the disruption element with the first aircraft has been resolved, such as by replacing a power source of the first electric aircraft. In one or more embodiments, charging connection may be reset so that charging connection  112  may be activated or restarted. For example, and without limitation, a user may manually override control circuit  120  and activate charging connection  112  to reestablish communication between charger  104  and electric vehicle  116 . In another example, and without limitation, disruption element  140  may be resolved, such as a battery is allowed to cool to an acceptable temperature or a hose connection is properly sealed, and control circuit  120  may determine that there is no longer a disruption element present and thus automatically reactivate charging connection  112 . 
     Referring now to  FIG.  2   , an embodiment of sensor suite  200  is presented in accordance with one or more embodiments of the present disclosure. The herein disclosed system and method may comprise a plurality of sensors in the form of individual sensors or a sensor suite working in tandem or individually. A sensor suite may include a plurality of independent sensors, as described herein, where any number of the described sensors may be used to detect any number of physical or electrical quantities associated with an aircraft power system or an electrical energy storage system. Independent sensors may include separate sensors measuring physical or electrical quantities that may be powered by and/or in communication with circuits independently, where each may signal sensor output to a control circuit such as a user graphical interface. In a non-limiting example, there may be four independent sensors communicatively connected to charging connection  112  measuring operating conditions of the communication such as temperature, electrical characteristic such as voltage, amperage, resistance, or impedance, or any other parameters and/or quantities as described in this disclosure. In an embodiment, use of a plurality of independent sensors may result in redundancy configured to employ more than one sensor that measures the same phenomenon, those sensors being of the same type, a combination of, or another type of sensor not disclosed, so that in the event one sensor fails, the ability of sensor  108  to detect phenomenon is maintained. 
     Sensor suite  200  includes a moisture sensor  204 . “Moisture”, as used in this disclosure, is the presence of water, this may include vaporized water in air, condensation on the surfaces of objects, or concentrations of liquid water. Moisture may include humidity. “Humidity”, as used in this disclosure, is the property of a gaseous medium (almost always air) to hold water in the form of vapor. An amount of water vapor contained within a parcel of air can vary significantly. Water vapor is generally invisible to the human eye and may be damaging to electrical components. There are three primary measurements of humidity, absolute, relative, specific humidity. “Absolute humidity,” for the purposes of this disclosure, describes the water content of air and is expressed in either grams per cubic meters or grams per kilogram. “Relative humidity”, for the purposes of this disclosure, is expressed as a percentage, indicating a present stat of absolute humidity relative to a maximum humidity given the same temperature. “Specific humidity”, for the purposes of this disclosure, is the ratio of water vapor mass to total moist air parcel mass, where parcel is a given portion of a gaseous medium. Moisture sensor  204  may be psychrometer. Moisture sensor  204  may be a hygrometer. Moisture sensor  204  may be configured to act as or include a humidistat. A “humidistat”, for the purposes of this disclosure, is a humidity-triggered switch, often used to control another electronic device. Moisture sensor  204  may use capacitance to measure relative humidity and include in itself, or as an external component, include a device to convert relative humidity measurements to absolute humidity measurements. “Capacitance”, for the purposes of this disclosure, is the ability of a system to store an electric charge, in this case the system is a parcel of air which may be near, adjacent to, or above a battery cell. 
     With continued reference to  FIG.  2   , sensor suite  200  may include electrical sensors  208 . Electrical sensors  208  may be configured to measure voltage of charging connection  112 , electrical current of charging connection  112 , and resistance of charging connection  112 . Electrical sensors  208  may include separate sensors to measure each of the previously disclosed electrical characteristics such as voltmeter, ammeter, and ohmmeter, respectively. 
     Alternatively or additionally, and with continued reference to  FIG.  2   , sensor suite  200  may include a sensor or plurality thereof that may detect voltage and direct the charging of individual battery cells of a power source according to charge level; detection may be performed using any suitable component, set of components, and/or mechanism for direct or indirect measurement and/or detection of voltage levels, including without limitation comparators, analog to digital converters, any form of voltmeter, or the like. Sensor suite  200  and/or a control circuit incorporated therein and/or communicatively connected thereto may be configured to adjust charge to one or more battery cells as a function of a charge level and/or a detected parameter. For instance, and without limitation, sensor suite  200  may be configured to determine that a charge level of a battery cell of a power source is high based on a detected voltage level of that battery cell or portion of the power source and/or battery pack. Sensor suite  200  may alternatively or additionally detect a charge reduction event, defined for purposes of this disclosure as any temporary or permanent state of a battery cell requiring reduction or cessation of charging; a charge reduction event may include a cell being fully charged and/or a cell undergoing a physical and/or electrical process that makes continued charging at a current voltage and/or current level inadvisable due to a risk that the cell will be damaged, will overheat, or the like. Detection of a charge reduction event may include detection of a temperature, of the cell above a threshold level, detection of a voltage and/or resistance level above or below a threshold, or the like. Sensor suite  200  may include digital sensors, analog sensors, or a combination thereof. Sensor suite  200  may include digital-to-analog converters (DAC), analog-to-digital converters (ADC, A/D, A-to-D), a combination thereof, and the like. 
     With continued reference to  FIG.  2   , sensor suite  200  may include thermocouples, thermistors, thermometers, passive infrared sensors, resistance temperature sensors (RTD&#39;s), semiconductor based integrated circuits (IC), a combination thereof or another undisclosed sensor type, alone or in combination. Temperature, for the purposes of this disclosure, and as would be appreciated by someone of ordinary skill in the art, is a measure of the heat energy of a system. Temperature, as measured by any number or combinations of sensors present within sensor suite  200 , may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin (° K), or another scale alone or in combination. The temperature measured by sensors may comprise electrical signals which are transmitted to their appropriate destination wireless or through a wired connection. 
     With continued reference to  FIG.  2   , sensor suite  200  may include a sensor configured to detect gas that may be emitted during or after a cell failure. “Cell failure”, for the purposes of this disclosure, refers to a malfunction of a battery cell of a power source, which may be an electrochemical cell, that renders the cell inoperable for its designed function, namely providing electrical energy to at least a portion of an electric aircraft. Byproducts of cell failure  212  may include gaseous discharge including oxygen, hydrogen, carbon dioxide, methane, carbon monoxide, a combination thereof, or another undisclosed gas, alone or in combination. Further the sensor configured to detect vent gas from electrochemical cells may comprise a gas detector. For the purposes of this disclosure, a “gas detector” is a device used to detect a gas is present in an area. Gas detectors, and more specifically, the gas sensor that may be used in sensor suite  200 , may be configured to detect combustible, flammable, toxic, oxygen depleted, a combination thereof, or another type of gas alone or in combination. The gas sensor that may be present in sensor suite  200  may include a combustible gas, photoionization detectors, electrochemical gas sensors, ultrasonic sensors, metal-oxide-semiconductor (MOS) sensors, infrared imaging sensors, a combination thereof, or another undisclosed type of gas sensor alone or in combination. Sensor suite  200  may include sensors that are configured to detect non-gaseous byproducts of cell failure  212  including, in non-limiting examples, liquid chemical leaks including aqueous alkaline solution, ionomer, molten phosphoric acid, liquid electrolytes with redox shuttle and ionomer, and salt water, among others. Sensor suite  200  may include sensors that are configured to detect non-gaseous byproducts of cell failure  212  including, in non-limiting examples, electrical anomalies as detected by any of the previous disclosed sensors or components. 
     With continued reference to  FIG.  2   , sensors  208  may be disposed on a sense board  216 . In one or more embodiments, sense board  216  may include opposing flat surfaces and may be configured to cover a portion of a battery module within a power source, such as a battery pack. Sense board  216  may include, without limitation, a control circuit configured to perform and/or direct any actions performed by sense board  216  and/or any other component and/or element described in this disclosure. Sense board  216  may be consistent with the sense board disclosed in U.S. patent application Ser. No. 16/948,140 entitled, “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE” and incorporated herein by reference in its entirety. 
     With continued reference to  FIG.  2   , sensor suite  200  may be configured to detect events where voltage nears an upper voltage threshold or lower voltage threshold. The upper voltage threshold may be stored in a memory of, for example, a computing device for comparison with an instant measurement taken by any combination of sensors present within sensor suite  200 . The upper voltage threshold may be calculated and calibrated based on factors relating to battery cell health, maintenance history, location within battery pack, designed application, and type, among others. Sensor suite  200  may measure voltage at an instant, over a period of time, or periodically. Sensor suite  200  may be configured to operate at any of these detection modes, switch between modes, or simultaneous measure in more than one mode. Sensor  108  may detect through sensor suite  200  events where voltage nears the lower voltage threshold. The lower voltage threshold may indicate power loss to or from an individual battery cell or portion of the battery pack. Sensor  108  may detect through sensor suite  200  events where voltage exceeds the upper and lower voltage threshold. Events where voltage exceeds the upper and lower voltage threshold may indicate battery cell failure or electrical anomalies that could lead to potentially dangerous situations for aircraft and personnel that may be present in or near its operation. Additional disclosure related to a battery management system may be found in U.S. patent application Ser. No. 17/111,002 and Ser. No. 17/108,798 entitled “SYSTEMS AND METHODS FOR A BATTERY MANAGEMENT SYSTEM INTEGRATED IN A BATTERY PACK CONFIGURED FOR USE IN ELECTRIC AIRCRAFT”, both of which are incorporated in their entirety herein by reference. 
     Exemplary methods of signal processing may include analog, continuous time, discrete, digital, nonlinear, and statistical. Analog signal processing may be performed on non-digitized or analog signals. Exemplary analog processes may include passive filters, active filters, additive mixers, integrators, delay lines, compandors, multipliers, voltage-controlled filters, voltage-controlled oscillators, and phase-locked loops. Continuous-time signal processing may be used, in some cases, to process signals which varying continuously within a domain, for instance time. Exemplary non-limiting continuous time processes may include time domain processing, frequency domain processing (Fourier transform), and complex frequency domain processing. Discrete time signal processing may be used when a signal is sampled non-continuously or at discrete time intervals (i.e., quantized in time). Analog discrete-time signal processing may process a signal using the following exemplary circuits sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. Digital signal processing may be used to process digitized discrete-time sampled signals. Commonly, digital signal processing may be performed by a computing device or other specialized digital circuits, such as without limitation an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a specialized digital signal processor (DSP). Digital signal processing may be used to perform any combination of typical arithmetical operations, including fixed-point and floating-point, real-valued and complex-valued, multiplication and addition. Digital signal processing may additionally operate circular buffers and lookup tables. Further non-limiting examples of algorithms that may be performed according to digital signal processing techniques include fast Fourier transform (FFT), finite impulse response (FIR) filter, infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters. Statistical signal processing may be used to process a signal as a random function (i.e., a stochastic process), utilizing statistical properties. For instance, in some embodiments, a signal may be modeled with a probability distribution indicating noise, which then may be used to reduce noise in a processed signal. 
     Now referring to  FIG.  3   , a flow chart of an exemplary method  300  for an immediate shutdown of charging for an electric vehicle charger is shown in accordance with one or more embodiments of the present disclosure. As shown in block  305 , method  300  includes identifying, by sensor  108  communicatively connected to electric vehicle charging connection  112 , communication of charging connection  112  between charger  104  and electric vehicle  116 . In one or more embodiments, communication between charger  104  and electric vehicle  116  includes an electric communication. In other embodiments, communication between the charger and the electric vehicle includes a mechanic communication. 
     As shown in block  310 , method  300  includes detecting, by sensor  108 , a charging characteristic  132  of communication. As shown in block  315 , method  300  includes generating, by sensor  108 , charging datum based on charging characteristic  132 . As shown in block  320 , method  300  includes receiving, by a processor communicatively connected to sensor  108 , charging datum * 136  of sensor  108 . 
     As shown in block  325 , method  300  includes determining, by control circuit  120 , disruption element  140  as a function of charging datum  136 . As shown in block  330 , method  300  includes disabling, by control circuit  120 , charging connection  112  based on disruption element  140 . In one or more embodiments, the disabling of charging connection  112  further includes terminating an electric communication between electric vehicle  116  and charger  104 . In one or more embodiments, method  300  further includes reactivating, by control circuit  120 , charging connection once disruption element  140  is resolved. In one or more embodiments, method  300  further includes generating, by the control circuit  120 , disablement command  144 . In one or more embodiments, method  3090  further includes preventing, by control circuit  120 , a second communication between the charger and the electric vehicle. In one or more embodiments, electric vehicle  116  may be an electric aircraft. 
     Now referring to  FIG.  4   , an exemplary embodiment of an electric vehicle, such as electric aircraft  400 , is shown in accordance with one or more embodiments of the present disclosure. In one or more embodiments, electric aircraft  400  may include a vertical takeoff and landing aircraft (eVTOL). As used herein, a “vertical take-off and landing (eVTOL) aircraft” is one that can 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. In some embodiments, an eVTOL aircraft  400  includes at least an aircraft component. As used in this disclosure, an “aircraft component” is any part of an aircraft, for example without limitation pilot controls, sensors, flight components, propulsors, landing gear, and the like. 
     Still referring to  FIG.  4   , electric aircraft  400  may include a power source, such as a battery pack. As previously mentioned in this disclosure, a power source may include a battery pack, which is configured to store electrical energy in the form of a plurality of battery modules, which themselves include of a plurality of electrochemical cells also referred to herein as battery 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, this disclosure will focus on the former. 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, may be electrically connected in series, in parallel or a combination of series and parallel. Series connection includes wiring a first terminal of a first cell to a second terminal of a second cell and further configured to include 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 include 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 includes 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 include more than one conductive path for electricity to flow while maintaining the same potential (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. 
     Referring now to  FIG.  5   , an exemplary embodiment of a machine-learning module  700  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  504  to generate an algorithm that will be performed by a computing device/module to produce outputs  508  given data provided as inputs  512 ; 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.  5   , “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  504  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  504  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  504  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  504  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  504  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  504  may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data  504  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.  5   , training data  504  may include one or more elements that are not categorized; that is, training data  504  may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data  504  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  504  to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data  504  used by machine-learning module  500  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, charging datum  136  of sensor  108  may be an input and disruption element  140  and/or disablement command  144  may be an output. 
     Further referring to  FIG.  5   , 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  516 . Training data classifier  516  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  500  may generate a classifier using a classification algorithm, defined as a process whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data  504 . 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. Training data classifier may include, but not limited to, different levels or power capabilities of an electric grid, different levels of failure modes of an electric grid, and the like. 
     Still referring to  FIG.  5   , machine-learning module  500  may be configured to perform a lazy-learning process  520  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  504 . Heuristic may include selecting some number of highest-ranking associations and/or training data  504  elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naive 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.  5   , machine-learning processes as described in this disclosure may be used to generate machine-learning models  524 . 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  524  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  524  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  504  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. 
     In one or more embodiments, and without limitation, a disruption element may be determined as a function of at least charging datum  136 . For example, and without limitation, a control circuit, such as computing device  516 , may be configured to train an immediate shutdown machine-learning model using disruption training data, where the disruption training data includes a plurality of charging data elements correlated with threshold elements. Computing device  516  may then be configured to generate disruption element as a function of the immediate shutdown machine-learning model. For example, and without limitation, immediate shutdown machine-learning model may relate charging data with one or more preconfigured thresholds to determine a corresponding disruption element and related disablement command. 
     Still referring to  FIG.  5   , machine-learning algorithms may include at least a supervised machine-learning process  528 . At least a supervised machine-learning process  528 , 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 operating states, 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  404 . 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  528  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.  5   , machine-learning processes may include at least an unsupervised machine-learning processes  532 . 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.  5   , machine-learning module  500  may be designed and configured to create a machine-learning model  524  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.  5   , 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 naive 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. 
     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 can 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.  6    shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system  600  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  600  includes a processor  604  and a memory  608  that communicate with each other, and with other components, via a bus  612 . Bus  612  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  604  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  604  may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor  604  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  608  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  616  (BIOS), including basic routines that help to transfer information between elements within computer system  600 , such as during start-up, may be stored in memory  608 . Memory  608  may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software)  620  embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory  608  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  600  may also include a storage device  624 . Examples of a storage device (e.g., storage device  624 ) 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  624  may be connected to bus  612  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 1394 (FIREWIRE), and any combinations thereof. In one example, storage device  624  (or one or more components thereof) may be removably interfaced with computer system  600  (e.g., via an external port connector (not shown)). Particularly, storage device  624  and an associated machine-readable medium  628  may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system  600 . In one example, software  620  may reside, completely or partially, within machine-readable medium  628 . In another example, software  620  may reside, completely or partially, within processor  604 . 
     Computer system  600  may also include an input device  632 . In one example, a user of computer system  600  may enter commands and/or other information into computer system  600  via input device  632 . Examples of an input device  632  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  632  may be interfaced to bus  612  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  612 , and any combinations thereof. Input device  632  may include a touch screen interface that may be a part of or separate from display  636 , discussed further below. Input device  632  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  600  via storage device  624  (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device  640 . A network interface device, such as network interface device  640 , may be utilized for connecting computer system  600  to one or more of a variety of networks, such as network  644 , and one or more remote devices  648  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  644 , may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software  620 , etc.) may be communicated to and/or from computer system  600  via network interface device  640 . 
     Computer system  600  may further include a video display adapter  652  for communicating a displayable image to a display device, such as display device  636 . 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  652  and display device  636  may be utilized in combination with processor  604  to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system  600  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  612  via a peripheral interface  656 . 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 can 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.