An approach is provided for dynamically controlling power distribution amongst a plurality of charging station ports based on one or more objectives. A method includes obtaining input data, wherein the input data includes at least one of: user data associated with one or more current charging station users, or non-user data that is not associated with the current charging station users. The method includes processing the input data through one or more machine learning engines, wherein the one or more machine learning engines are trained to determine a particular power distribution, among the plurality of charging station ports, that achieves one or more objectives. The method includes configuring the plurality of charging station ports according to the particular power distribution. The particular power distribution specifies a maximum charging rate or a percentage of a power budget for each of the plurality of charging station ports.

In one aspect, a method comprises receiving first data pertaining to a battery pack of a vehicle, wherein the first data is received from sensors associated with the vehicle, and the first data pertains to a battery pack current, a cell voltage, a cell current, a cell temperature, or some combination thereof; receiving second data pertaining to simulation of the battery pack; receiving third data from a manufacturer; receiving historical data on a fleet of vehicles that use the battery pack; predicting a remaining useful life of the battery pack of the vehicle by using a hybrid model comprising a physics-based model that receives the based on the first, second, third, and historical data, and generates properties pertaining to the battery pack; a machine learning model that uses the properties to predict the remaining useful life of each cell of the battery pack; and transmitting the remaining useful life.

A system for mitigating charging failure for an electric aircraft including a charging connector, a sensor, a charger, and a controller. The charging connector comprising at least a charging pin and configured to mate with a corresponding charging port on an electric aircraft. The sensor communicatively connected to the charging connector and configured to detect a charging datum. The charger electrically connected to the charging connector and comprising a power source electrically connected to the charging connector. The controller communicatively connected to the sensor and configured to receive a charging datum from the sensor, detect a charging failure as a function of the charging datum, and initiate a mitigating response in response to detecting a charging failure.

A method for operating an assistance system for a motor vehicle involves providing a configuration set personalized for a user of the assistance system in an electronic computing device of the assistance system for at least one functional unit of the motor vehicle. The personalized configuration set is set by the electronic computing device depending on a triggering criterion. The user is identified as the trigger criterion by a first sensor device and/or a predetermined piece of information relating to the motor vehicle is detected as the trigger criterion by a second sensor device.

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

Various implementations include approaches for training a surface detection classifier and detecting characteristics of a surface, along with related micromobility vehicles. Certain implementations include a method including: comparing: i) detected movement of a micromobility (MM) vehicle or a device located with a user at the MM vehicle while operating the MM vehicle, with ii) a surface detection classifier for the MM vehicle; and in response to detecting that the MM vehicle is traveling on a restricted surface type for a threshold period, performing at least one of: a) notifying an operator of the MM vehicle about the travel on the restricted surface type, b) outputting a warning at an interface connected with the MM vehicle or the device, c) limiting a speed of the MM vehicle, or d) disabling operation of the MM vehicle.

A system for establishing a primary function display for an electrical vertical takeoff and landing aircraft. The system further includes a plurality of sensors that detects at least a metric and generates at least a datum based on the at least a metric. Specifically, state of charge is at least generated based on the performance metric of an energy source. The system further includes a display to show the at least a datum. The system further includes a controller that receives the at least a datum and generates a visual to the pilot.

A system for determining remaining useful energy in an electric aircraft, the system including a computing device where the computing device is configured to measure a internal state datum of a battery as a function of at least a sensor, receive the internal state datum from the at least a sensor, generate a useful energy remaining datum as a function of the internal state datum and a battery model, and display the useful energy remaining datum to a user.

A connector for charging an electric vehicle that includes a housing configured to mate with an electric vehicle port of an electric vehicle, at least a sensor configured to detect an attachment datum as a function of the housing mating with an electric vehicle port, and transmit the attachment datum to a computing device, a computing device configured to receive the attachment datum from the at least a sensor, receive an identification datum from the electric vehicle, generate a verification datum as a function of the identification datum and the attachment datum, and determine an authorization status as a function of the verification datum.

A control system includes one or more processors that obtain event data associated with a vehicle. The processors also receive a statement including a designated chargeable usage of the vehicle, and access one or more rules dictating rule-based chargeable usage of the vehicle. The processors may compare the event data with the one or more rules to determine the rule-based chargeable usage of the vehicle based on the event data and to compare the rule-based chargeable usage with the designated chargeable usage to identify one or more discrepancies between the rule-based chargeable usage and the designated chargeable usage. Further, the processors may, responsive to identifying the one or more discrepancies, generate a reconciliation request for correcting the one or more discrepancies and communicate the reconciliation request to the entity that issued the statement.