A method, apparatus, and computer program product are provided for predicting range of an electric vehicle. The system may comprise at least one memory configured to store computer program code and at least one processor configured to execute the computer program code to at least determine future location prediction data for the electric vehicle based on a mobility profile, wherein the mobility profile comprises with historical usage data for the electric vehicle. The computer program code further comprises code to retrieve weather data from a weather service provider, wherein the weather data is associated with the future location prediction data of the electric vehicle and the weather data includes at least temperature data associated with the future location prediction data of the electric vehicle. Further, the computer program code comprises code to calculate a range prediction value for the electric vehicle based on the future location prediction data and the weather data, for predicting the range of the electric vehicle. Also, the computer program code comprises code to provide a notification associated with the predicted range of the electric vehicle to a user device.

Methods and systems are provided for cloud processing data streamed from a vehicle and a home (e.g., any location) associated with a user account. One method includes receiving a data stream from the vehicle entity, where the data stream from the vehicle entity includes metadata from one or more data producing objects of the vehicle entity. And, receiving a data stream from the home entity, where the data stream from the home entity includes metadata from one or more data producing objects of the home entity. The method includes accessing action conditions associated with a user account. The action conditions identify a position where at least one or more states of the metadata from each of the home entity and the vehicle entity intersect. And, each action condition identifies a type or types of control information to be processed. The method includes processing the received metadata from the vehicle entity and the home entity. The processing identifies metadata of the home entity and the vehicle entity that includes an intersection of said at least one or more states of said respective metadata of the home entity and the vehicle entity. The intersection is indicative that a specific action condition being satisfied. The method includes sending, in response to the specific action condition being satisfied, control information to the user account. The logic associated with the user account determines when the control information is sent to the vehicle entity or the home entity for surfacing information or making a setting regarding the satisfied specific action condition. Intersections can also be identified with user devices that may be associated with the user account.

A network of collection, charging and distribution machines collect, charge and distribute portable electrical energy storage devices (e.g., batteries, supercapacitors or ultracapacitors). To charge, the machines employ electrical current from an external source, such as the electrical grid or an electrical service of an installation location. The charging and distribution machines may distribute portable electrical energy storage devices of particular performance characteristics and other attributes based on customer preferences and/or customer profiles. The charging and distribution machines may provide instructions to or otherwise program portable electrical energy storage devices stored within the charging and distribution machines to perform at various levels according to user preferences and user profiles.

Presented are intelligent vehicle systems and control logic for driver coaching and on-demand vehicle charging, methods for making/using such systems, and motor vehicles with real-time eco-routing and automated driving capabilities. A method for controlling operation of a vehicle includes: determining an origin and destination for the vehicle; conducting a geospatial query to identify a candidate route for traversing from the origin to the destination; determining, based on current electrical characteristics of the vehicle's battery pack, an estimated driving range for the vehicle; responsive to the estimated driving range being less than the candidate route's distance, evaluating energy characteristics of the candidate route to derive an estimated energy expenditure to reach the destination; using the estimated energy expenditure, generating an action plan with vehicle maneuvering and/or accessory usage actions that extend the estimated driving range; and commanding a resident vehicle subsystem to execute a control operation based on the action plan.

A vehicle includes an electric machine and a controller. The electric machine is configured to draw energy from a battery to propel the vehicle and to recharge the battery during regenerative braking. The controller is programmed to, in response to identifying a regenerative braking opportunity along an upcoming road segment based on a classification of driver behavior and a classification of the upcoming road segment, operate the electric machine to recharge the battery along the upcoming road segment.

An adjustable vehicle traction battery charge setpoint strategy is disclosed which enables the adjustment of the maximum battery State of Charge (SOC) setpoint used for battery charging from an electric utility grid. Based on knowledge of the upcoming route and related driving behavior, the vehicle calculates a setpoint less than the maximum battery SOC charging setpoint so that when the vehicle begins operation, it can utilize regenerative braking and historical driving behavior to allow the battery charge to be maximized during the trip.

A vehicle battery heating apparatus is to be mounted in a vehicle that travels with electricity from a battery. The apparatus includes a battery, a heater, a sensor, a controller, and a memory. The battery is mountable in the vehicle to allow the vehicle to travel. The heater heats the battery. The sensor obtains a temperature of the battery. The controller heats the battery with the heater. The memory stores heating target temperatures for respective remaining capacities of the battery. The controller obtains a remaining capacity of the battery, obtains a target temperature from the memory in accordance with the obtained remaining capacity, and sets a target temperature for heating the battery which is not on charge while the vehicle is stopped to the obtained target temperature. The heater heats the battery to the set target temperature.

A method, apparatus, and computer program product are provided for predicting range of an electric vehicle. The system may comprise at least one memory configured to store computer program code and at least one processor configured to execute the computer program code to at least determine future location prediction data for the electric vehicle based on a mobility profile, wherein the mobility profile comprises with historical usage data for the electric vehicle. The computer program code further comprises code to retrieve weather data from a weather service provider, wherein the weather data is associated with the future location prediction data of the electric vehicle and the weather data includes at least temperature data associated with the future location prediction data of the electric vehicle. Further, the computer program code comprises code to calculate a range prediction value for the electric vehicle based on the future location prediction data and the weather data, for predicting the range of the electric vehicle. Also, the computer program code comprises code to provide a notification associated with the predicted range of the electric vehicle to a user device.

A method for operating a motor vehicle includes the steps of registering a maximum dynamics requirement and adapting the idling operating mode of a fuel cell system of the motor vehicle on the basis of the maximum dynamics requirement. In the first maximum dynamics requirement, lower dynamics are required than in the second maximum dynamics requirement. In the first maximum dynamics requirement, the fuel cell system is operated in a first idling operating mode. In the second maximum dynamics requirement the fuel cell system is operated in a second idling operating mode. The fuel cell system is operated more efficiently in the first idling operating mode than in the second idling operating mode.

Presented are intelligent vehicle systems and control logic for driver coaching and on-demand vehicle charging, methods for making/using such systems, and motor vehicles with real-time eco-routing and automated driving capabilities. A method for controlling operation of a vehicle includes: determining an origin and destination for the vehicle; conducting a geospatial query to identify a candidate route for traversing from the origin to the destination; determining, based on current electrical characteristics of the vehicle's battery pack, an estimated driving range for the vehicle; responsive to the estimated driving range being less than the candidate route's distance, evaluating energy characteristics of the candidate route to derive an estimated energy expenditure to reach the destination; using the estimated energy expenditure, generating an action plan with vehicle maneuvering and/or accessory usage actions that extend the estimated driving range; and commanding a resident vehicle subsystem to execute a control operation based on the action plan.