A ground utility robot and implement attachment apparatus having a ground utility robot, at least one implement, at least one solar panel, at least one battery that is chargeable by the at least one solar panel, a power take-off system that is connected to the ground utility robot and to the at least one implement; where the battery powers said ground utility robot and the implement; a safety system that has a computer, a safety program that utilizes a processing logic on the computer, where the safety program initiates precautionary measures that are carried out by the ground utility robot and the power take-off system if an object comes within a predefined distance from the ground utility robot and implement attachment apparatus.

A vehicle includes a battery, an electric machine, and a controller. The battery has a state of charge. The electric machine is configured to draw electrical power from the battery to propel the vehicle in response to an acceleration request and to deliver electrical power to the battery to recharge the battery. The controller is programmed to adjust an estimation of battery state of charge based on a feed forward control that includes a coulomb counting algorithm, a first feedback control that includes a first battery model, and a second feedback control that includes a second battery model. The controller is further programmed to control the electrical power flow between the battery and the electric machine based on the estimation of the state of charge of the battery.

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.

The disclosed systems and methods provide a novel framework that provides mechanisms for a hands-free, autonomous electrical connection of an electric charger to an electric vehicle (EV), and subsequent charging. The disclosed framework utilizes an automated connection device (ACD) as an intermediary between the charger and the EV. The ACD is configured for automatically determining a precise location of the charging inlet on the EV and then automatically establishing an electrical connection with the EV so that the EV can receive a charge. The ACD performs the disclosed precise positional and directional navigation to the EV inlet based on deep neural network analysis of captured imagery of the inlet. In some embodiments, the images can be modified so as to highlight and/or assist the ACD's navigation towards to the inlet in order to maximize invariance.

A system and method for a recharging station including an elevated landing pad, a rechargeable component coupled to the elevated landing pad, a power delivery unit configured to deliver power from a power supply unit or power storage unit to the recharging component, and a support component coupled to the bottom of the elevated landing pad.

The present invention provides an artificial intelligence-based system for management of electric vehicles fleet. The system receives live data and historical data feeds from charging stations, fleet telematics, meteorological services, traffic management, mobile application, fleet dashboard, renewable source of energy, battery energy storage system, and the electric utility grid. The system utilizes machine learning algorithms to predict energy usage and optimize the charging schedule of electric vehicle. The system uses real time data to generate electric vehicle trip condition training feature for predicting the remaining driving range. The system predicts the vehicle's arrival time at the charging station based on telematics data of each vehicle collected from the fleet management system.

An example operation includes one or more of establishing a communication channel between a computing system associated with a plurality of available power sources and a transport comprising a rechargeable battery that is configured to power the transport, determining a value of charge power for the rechargeable battery, generating a request that identifies the value of charge power in a first field and identifies a power source in a second field from among a plurality of available power sources to source the charge power for the rechargeable battery, and transmitting the request from the transport to a computing system via the established communication channel.

A vehicle control system and method include processors that determine that an energy storage device of a vehicle will have insufficient energy to power a propulsion system of the vehicle under a first set of operational settings to move the vehicle from a first location within a powered segment to a designated second location that is outside of an unpowered segment of a route. Responsive to determining that the energy storage device will have insufficient energy, the processors change one or more settings of the vehicle to operate the vehicle under a second set of operational settings while the vehicle moves within the powered segment of the route to charge the energy storage device to a greater extent relative to operating of the vehicle according to the first set of operational settings.

A battery management system includes one or more processors, a battery comprising a plurality of cells, an output device, an input device, and a memory having an input module, a battery characteristic prediction module, and an output module. The input module includes instructions that cause the one or more processors to receive a mode selection from a user via the input device. The battery characteristic prediction module includes instructions that cause the one or more processors to predict a characteristic of the battery based on the mode selection using an active machine learning model to predict the characteristic of the battery. The output module includes instructions that cause the one or more processors to output an estimated cost to the output device based on the characteristic of the battery determined by the active machine learning model.

A system, method and computer program product for communicating data of a vehicle. The system includes a data recording device and a data transmitting device coupled to the vehicle. The data recording device is configured to receive at least a vehicle datum during operation of the vehicle, determine a subset of the at least a vehicle datum to be stored, wherein the subset of data is stored during operation of the vehicle, and store the subset of the at least a vehicle datum as a stored vehicle data, wherein the stored vehicle data comprises a diagnostic datum of a vehicle component. The data transmitting device is configured to initiate communication with an independent data communication device as a function of a connection to a network and transmit the stored vehicle data to the independent data communication device as a function of the connection to the network.