Abstract: A method and system for smart selection of a charging station is contemplated. The selection may be based on a charging recommendation being generated in reply to a charging request for battery charging. The charging recommendation may identify one or more target charging stations suitable for the battery charging. The charging recommendation may include a state of health (SOH) matrix configured to report charging capabilities of the target charging stations according to vehicle and station charging metrics associated therewith.

Abstract: A computer-implemented method for scheduling pre-departure charging for electric vehicles includes predicting a user-departure time based on a first machine learning prediction model. The method further includes determining a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model. The method further includes determining a battery-temperature to be set at the user-departure time based on a third machine learning prediction model. The method further includes determining a present charge level of a battery of the electric vehicle. The method further includes computing a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature. The method further includes initiating charging the battery at the charging start-time.

Abstract: An economy advisory system includes a circuit and a display. The circuit is configured to receive a route plan remainder of a vehicle and receive a plurality of consumption factor values of the vehicle while the vehicle traverses the route plan. The plurality of consumption factor values is controllable by a driver of the vehicle. The circuit is further configured to modify the plurality of consumption factor values based upon a plurality of modification factor values, calculate a plurality of potential range changes of the vehicle based on the route plan remainder and the plurality of consumption factor values as modified, and identify a group that has at least one of the plurality of potential range changes and a corresponding at least one of the plurality of modification factor values. The display is in communication with the circuit and is configured to present the group to the driver.

Abstract: A system includes a memory device, and one or more processors for detection, characterization, and presentation of charging stations for electric vehicles. The processors determine that a charging station is an undocumented charging station; a documented charging station is one that is part of a dataset of known charging stations. A confidence score is computed to indicate whether the charging station is a public charging station. In response to the confidence score being greater than a first predetermined threshold, the undocumented charging station is documented as a public charging station. In response to the confidence score being lesser than the first predetermined threshold and greater than a second predetermined threshold, the undocumented charging station is added to a list of charging stations to investigate. In response to the confidence score being lesser than the second predetermined threshold, the undocumented charging station is documented as a private charging station.

Abstract: A computer-implemented method for scheduling pre-departure charging for electric vehicles includes predicting a user-departure time based on a first machine learning prediction model. The method further includes determining a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model. The method further includes determining a battery-temperature to be set at the user-departure time based on a third machine learning prediction model. The method further includes determining a present charge level of a battery of the electric vehicle. The method further includes computing a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature. The method further includes initiating charging the battery at the charging start-time.