A distance-to-empty presentation apparatus of a fuel cell vehicle includes: a traveling speed acquisition unit configured to acquire a traveling speed of the fuel cell vehicle; a traveling distance acquisition unit configured to acquire a traveling distance of the fuel cell vehicle in a fuel filling period, the fuel filling period is a period from the time when the fuel is filled in the fuel cell vehicle previously to the time when the fuel is filled in the fuel cell vehicle this time; a fuel residual amount acquisition unit configured to acquire a fuel residual amount of the fuel cell vehicle; a fuel consumption amount acquisition unit configured to acquire a fuel consumption amount of the fuel cell vehicle; a fuel efficiency calculation unit configured to calculate fuel filling period fuel efficiency by using the traveling distance in the fuel filling period and a traveling period fuel consumption amount that is the fuel consumption amount during traveling preparation and during traveling in the fuel filling period, wherein the fuel filling period fuel efficiency is fuel efficiency in the fuel filling period; a distance-to-empty calculation unit configured to calculate a distance to empty of the fuel cell vehicle by using the fuel filling period fuel efficiency and the fuel residual amount; and a presentation device configured to present the distance to empty.

Methods, systems, devices and apparatuses for a vehicle control system. The vehicle control system includes a memory. The memory is configured to store multiple charging events that activate multiple charging plans. The vehicle control system includes a navigation unit that is configured to obtain a current location of the vehicle. The vehicle control system includes an electronic control unit. The electronic control unit is configured to determine that the vehicle is within a threshold distance of the first charging event. The electronic control unit is configured to control an operation of the vehicle to prepare the vehicle to charge or discharge the battery based on a first charging plan when the vehicle is within the threshold distance of a first charging event.

When a control unit assigns, to each of multiple routes, any of multiple vehicles that is able to travel with a drive source of at least one of an electric motor and an internal combustion engine, categories of the vehicles being different from each other, the control unit determines, based on a cost or an environmental load when the vehicle is caused to travel on the routes with the electric motor and a cost or an environmental load when the vehicle is caused to travel on the routes with the internal combustion engine, combinations of the vehicles and the routes.

To navigate an electric vehicle from a starting location to a destination, a system identifies multiple charging stations between the starting location and the destination and determining a navigation route that requires a least amount of time for the electric vehicle to travel from the starting location to the destination via one or more of the charging stations, including determining a non-linear relationship between an amount of time and an amount of charge the EV receives during the amount of time.

Route planning for a hybrid electric vehicle (HEV) includes obtaining respective engine activation actions for at least some road segments of a route between an origin and a destination by optimizing for at least one of a noise level or energy consumption of an engine of the HEV that is used to charge a battery of the HEV. The HEV is then controlled to follow the at least some of the road segments of the route and to activate the engine according to the respective engine activation actions. Controlling the HEV to follow the at least some of the road segments includes masking at least one of the respective engine activation actions for a current road segment by increasing a volume of an entertainment system of the HEV.

Systems, methods, and other embodiments described herein relate to informing a user how a hypothetical electric vehicle would perform based on the driving habits of the user in a non-electric vehicle. In one embodiment, a method includes identifying an origin and a destination of a non-electric vehicle after the non-electric vehicle has arrived at the destination. The method includes determining energy information for a hypothetical electric vehicle traveling from the origin to the destination and outputting the energy information to the user.

A method of optimizing rider satisfaction includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as indicative of an effect on a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, at least one aspect of rider satisfaction affected by an effect on the transportation system derived from the social media data classified as indicative of an effect on the transportation system. The method still further includes optimizing, using a third neural network of the hybrid neural network, the at least one aspect of rider satisfaction for at least one rider occupying a vehicle in the transportation system.

The present disclosure relates to an ISG control system and an ISG control method for a construction machine capable of controlling ISG even in a working state of a construction machine, the ISG control system including: a surrounding environment determinator configured to determine surrounding environment of the construction machine; and a vehicle controller configured to determine whether the construction machine is in a working state or a traveling state based on a determination result from the surrounding environment determinator, wherein the vehicle controller controls an engine based on a preset work ISG condition when it is determined that the construction machine is in the working state, and wherein the vehicle controller controls the engine based on a preset travel ISG condition when it is determined that the construction machine is in the traveling state.

Systems and methods of the present disclosure may collect data associated with a user activity. The data may be transmitted from an app running on a computing device with a user account authenticated by the computer-based system. The system may calculate a carbon footprint of the user activity based on the data associated with the user activity. An amount of carbon credits may be assigned to a user account authenticated with the computer-based system based on the calculated carbon footprint of the user activity. A transaction may be written to a blockchain retiring the amount of carbon credits in response to a request to offset a carbon footprint.

Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.