Embodiments described herein relate to predicting the utilization of electric vehicle (EV) charge points. Methods may include: receiving an indication of a plurality of candidate locations for EV charge points; determining static map features of the plurality of candidate locations; inputting the plurality of candidate locations and static map features into a machine learning model, where the machine learning model is trained on existing EV charge point locations, existing EV charge point static map features, and existing EV charge point utilization; determining, based on the machine learning model, a predicted utilization of an EV charge point at the plurality of candidate locations; and generating a representation of a map including the plurality of candidate locations, where candidate locations of the plurality of candidate locations are visually distinguished based on a respective predicted utilization of an EV charge point at the candidate locations.

A system for preconditioning a power source of an electric aircraft is presented. The apparatus includes a power source of an electric aircraft, a computing device, and a user device. The computing device is configured to receive a flight plan, determine a predicted power usage model as a function of the flight plan, and initiate a power source modification on the electric aircraft as a function of the predicted power usage model. The user device is configured to display a flight performance infographic as a function of the predicted power usage model.

Aspects relate to a system for flight plan generation of an electric vertical takeoff and landing (eVTOL) aircraft. An exemplary system for flight plan generation includes a flight controller mounted on an eVTOL aircraft. The flight controller may be configured to receive a plurality of flight plan data and generate a flight plan for the aircraft as a function of the plurality of flight plan data.

A control apparatus for a vehicle includes an offset torque calculator configured to perform calculation of offset torque to be applied to at least one wheel of the vehicle. The offset torque is required to stop the vehicle on a sloping road having a predetermined gradient. The control apparatus includes a motor controller configured to, for stopping the vehicle on the sloping road having the predetermined gradient, perform control of causing output torque of the motor-generator to asymptotically approach the offset torque.

A system and method for providing real-time distributed micro-grid optimization using price signals to the electrical grid system by allowing bi-directional electricity usage from a distributed network of energy storage stations to form a large, distributed resource for the grid. A machine learning optimization module ingests various forms of data-from grid telemetry to traffic data to trip-to-trip data and more-in order to make informed spatiotemporal decisions about optimal pricing signals as well as strategically placing and balancing energy stores across various regions to support optimum energy usage, risk mitigation, grid fortification, and revenue generation. Energy stores are then sent updated price signals and updated parameters as to the amount of energy to hold or release.

A highly safe power storage system is provided. If n (n is an integer over or equal to three) secondary batteries are used in a vehicle such as an electric vehicle, a circuit configuration is used with which the condition of each secondary battery is monitored using an anomaly detection unit; and if an anomaly such as a micro-short circuit is detected, only the detected anomalous secondary battery is electrically separated from the charging system or the discharging system. At least one microcomputer monitors anomalies in n secondary batteries consecutively, selects the anomalous secondary battery or the detected secondary battery which causes an anomaly, and gives an instruction to bypass the secondary battery with each switch.

In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

A temperature estimation system and method for an electric motor of a vehicle include a set of sensors configured to measure a set of operating parameters of the electric motor including at least (i) phase current, (ii) speed, and (iii) coolant temperature and a controller configured to access a trained artificial neural network (ANN) temperature estimation model, using the trained ANN temperature estimation model with the set of electric motor operating parameters as inputs, estimate temperatures of a stator and a rotor of the electric motor, and control operation of the electric motor based on the estimated stator and rotor temperatures.

A system for emergency shutdown of an electric charger in response to a disconnection is presented. The system includes a computing device, wherein the computing device is configured to receive a sensor datum from a sensor, determine a disruption element between a charging connector and an electric vehicle as a function of the sensor datum, and initiate a disconnection protocol as a function of the disruption element.

Techniques are presented for scheduling the charging of electric vehicles (EVs) that protect the resources of local low voltage distribution networks. From utilities, data on local low voltage distribution networks, such as the rating of a distribution transformer through which a group of EVs are supplied, is provided to a load manager application. Telematics information on vehicle usage is provided from the EVs, such as by way of the original equipment manufacturer. From these data, the load manager application determines schedules for charging the group of EVs through a shared low voltage distribution network so that the capabilities of the local low voltage distribution network are not exceeded while meeting the needs of the EV user. Charging schedules are then transmitted to the on-board control systems of the EVs for implementation.