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.

Techniques regarding parameterizing energy consumption of an electric vehicle are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a vehicle state estimation component that determines an operating condition experienced by a vehicle while traveling a route. Further, the system can comprise an energy consumption component that parametrizes an amount of energy expended by the vehicle while traveling the route based on a loss table that is populated with an energy consumption value derived from historic operation of the vehicle at the operating condition.

A method for determining a minimum state of charge for an energy storage means of a vehicle can include: determining a routine of use of charge of the energy storage means; determining a user requirement for future driving of the vehicle; predicting a reduction in the state of charge of the energy storage means associated with the user requirement in dependence on the determined routine; determining a minimum state of charge for the energy storage means for enabling the user requirement to be satisfied in dependence on the predicted reduction; and providing an output to the user indicative of a time requirement for increasing the state of charge of the energy storage means to a value at or above the minimum state of charge.

In some embodiments, an electric vehicle sends a vehicle charging request to a supervisory service. The electric vehicle receives, from the supervisory service in response to the vehicle charging request, a location of a telecommunication node of a telecommunication network that is configured to provide charging to electric vehicles. The electric vehicle navigates to the received location of the telecommunication node. The vehicle initiates charging of the electric vehicle at the location of the telecommunication node.

Systems and methods are described which provide targeted advertisements to a charging station for electric vehicles. A data collector records data associated with individuals near a charging station. A meter determines whether the charging station is being used to charge an electric vehicle. When the charging station is being used, a display displays advertisements targeted to the individuals. The targeted advertisements are selected from a database according to the data recorded by the data collector, and the database stores advertising content with discrete advertising segments that are electronically accessible. A processor is coupled to the data collector, the meter, and the display. The processor transmits the data recorded by the data collector, and receives the targeted advertisements.

Disclosed is an electrical vehicle control system for an electrical vehicle. The electrical vehicle control system includes a first system layer for hosting a software application management and infotainment arrangement for providing a graphical user interface for controlling operating parameters by a user of the electrical vehicle, a second system layer for communicating operational data between functional modules of the electrical vehicle, and a third modular layer including the functional modules of the electrical vehicle, wherein the first system layer is operable to control operating parameters provided by the second system layer for controlling driving characteristics of the electrical vehicle, wherein the driving characteristics include at least one of a selection of automatic or manual drive-train manners of operation, acceleration characteristics of the motor controller when providing power in operation to an electrical motor of the electrical vehicle, demand response functionality provided by the electrical vehicle when connected to a power grid, a manner of electrical power transfer between the battery unit and the electrical motor when the electrical vehicle is driven in operation and an economy of power utilization within the electrical vehicle in respect of cabin heating and battery unit charge depletion; and the second system layer includes a data communication bus for exchanging data between the functional modules, and the data communication bus is operable to provide data to the first system layer for the software application management and infotainment arrangement (SAMI) to perform data analysis, strategic control and/or reporting to a user of the electrical vehicle.

Method for determining predicted acceleration information which describes a future acceleration potential of an electric vehicle having an electric motor as the drive device, which is supplied with electric power from a battery in the electric vehicle, this method including the following steps: —Supplying power predictive information of the electric motor, which describes the predicted available acceleration power of the electric motor for at least one future period of time, —Determining the acceleration information from the power predictive information by using a vehicle model which supplies the prevailing operating state of the electric vehicle, at least one vehicle parameter describing the acceleration possible on the basis of the acceleration power and/or using predictive path data supplied in particular by a navigation system for the period of time.

A vehicle reach area presentation device sets a unit area in which a start point exists as a start area among unit areas into which a region on map data is divided and extracts pieces of probe data including records of past travels within a certain range from the start area. The device sets the unit areas, including goal points recorded in the extracted pieces of probe data, as reach areas, calculates energy consumptions from the start area to the reach areas by using the pieces of probe data, and presents the reach areas to a user while changing display of the reach areas depending on the calculated energy consumptions.

In one aspect, a system for charging an aircraft can include a robotic charging device, and a computing system configured to obtain data associated with a transportation itinerary and energy parameter(s) of the aircraft. The data associated with the transportation itinerary can be indicative of an aircraft landing facility at which the aircraft is to be located. The computing system can determine (e.g., select) a robotic charging device from among a plurality of robotic charging devices for charging the aircraft based on the transportation itinerary data and energy parameter(s) of the aircraft; determine charging parameter(s) for the robotic charging device based on the transportation itinerary data; and communicate command instruction(s) for the robotic charging device to charge the aircraft according to the charging parameter(s). The robotic charging device can be configured to automatically connect with a charging area of the aircraft for charging a battery onboard the aircraft.

The invention relates to a method and to a system for operating a fuel cell system (22) and at least one sub-system (30) of the fuel cell system (22). According to the invention, these are arranged in a vehicle (10), wherein the energy for a drive train (12) of the vehicle (10) can be drawn both from the fuel cell system (22) and from an alternative energy store (26). The method comprises the following method steps: first, the number and duration of shut-down and/or stop phases of the vehicles (10) in a defined time interval in a first vehicle state (86) or in a second vehicle state (88) is determined based on vehicle state-specific learning functions (90, 112). Operating parameters of the fuel cell system (22) and of the at least one sub-system (30) of the fuel cell system (22) are then adjusted in dependence on the determined number and duration of shut-down and/or stop phases of the vehicle (10).