A system and method for placement optimization of public electric vehicle charging stations using telematics data that includes receiving vehicle telematics data from a plurality of vehicles. The system and method also includes analyzing the vehicle telematics data to determine clusters of candidate locations of the public electric vehicle charging stations and selecting a subset of nodes of a fully connected graph structure that are associated with the candidate locations as optimal locations of the public electric vehicle charging stations. The system and method further include controlling an electronic computing system to present a human machine interface to present a visualization of the optimal locations of public electric vehicle charging stations to at least one party.

A plug connection system that autonomously charges an electric vehicle (EV) is provided. The method includes: obtaining a trained machine learning (ML) model from a back-end server; capturing an image using an image capturing device of the charging system, wherein a portion of the image comprises the EV charging portal; inputting the image into the trained ML model to determine one or more regions of interest associated with the EV charging portal within the image; determining a location of the EV charging portal based on the one or more determined regions of interest and one or more image processing techniques; and providing information to maneuver the robotic arm of the charging system to a physical position based on the determined location of the EV charging portal.

An example operation includes one or more of determining, by a transport, an energy transfer condition exists along a route, routing, by the transport, to a location on the route based on the energy transfer condition exceeding an energy transfer value and based on one or more traffic conditions, aligning, by the transport, a position of the transport at the location to wirelessly receive an energy transfer, and receiving, by the transport, the energy transfer while the transport is in motion.

A selection apparatus includes an acquirer configured to acquire information of a usage state of at least one reuse component before being reused and a purpose of use of the at least one reused component; and a selector configured to select a reused component suitable for the purpose of use based on the usage state.

A method for controlling an operating mode of a vehicle is presented. The method includes determining a current range of the vehicle while the vehicle is operating in a first operating mode. The method also includes determining a distance to a destination. The method further includes controlling the vehicle to operate in a second operating mode instead of the first operating mode when the range is less than the distance to the destination.

An equipment inspection system receives data captured by a sensor of an autonomous vehicle (AV). The captured data describes a current state of equipment for servicing the AV. The equipment inspection system compares the captured data to a model describing an expected state of the equipment. The equipment inspection system determines, based on the comparison, that the equipment differs from the expected state. The equipment inspection system may transmit data describing the current state of the equipment to an equipment manager. The equipment manager may schedule maintenance for the equipment based on the current state of the equipment.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon and simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized.

A system for restricting power to a load to prevent engaging a circuit protection device for an electric aircraft includes an energy source. The energy source is communicatively coupled to a load, wherein the load includes a portion of a propulsion system. The system includes sensors configured to sense an electrical parameter. The system includes an aircraft controller communicatively connected to the energy source, wherein the aircraft controller is configured to receive an electrical parameter, compare the electrical parameter to a current allocation threshold, detect that the electrical parameter has reached a current allocation threshold, generate a current allocation threshold notification as a function of the detection, wherein the current allocation threshold notification indicates that the electrical parameter has reached the current allocation threshold.

A controller selectively prevents electrical power flow from a traction battery to an electric machine based on an actual rate of charge acquired by a cell of the traction battery per unit of actual increase in amp hours and an expected rate of charge acquired per unit of expected increase in amp hours.

A method for determining MTPA, flux-weakening, and MTPV operating points over the full speed range of an IPM motor for the most efficient torque control of the motor using a neural network is provided. The neural network is trained using a cloud-based neural network training algorithm. A special technique is developed to generate neural network training data, that is particularly suitable and favorable, to develop a high-performance neural network-based IPM torque control system, and the impact of variable motor parameters is embedded into the neural network system development and training. The provided method can achieve a fast and accurate current reference generation with a simple neural network structure, for optimal torque control of an IPM motor. The method can handle the MTPA, MTPV, and flux-weakening operation considering physical motor constraints.