A system is provided for controlling engine fueling and includes an internal combustion engine, a fuel source, means for delivering fuel from the fuel source to the engine, and a controller configured to control the fuel delivering means according to a first operational mode so that, upon attaining a predetermined operational state of the engine, an amount of fuel delivered to the engine per unit time is kept constant. The system will typically but not necessarily be provided in a vehicle such as a truck or a passenger automobile. A method for controlling engine fueling is also provided.

In one aspect, a method of controlling gear shifting in response to a driving mode change, the method including determining a maximum number of allowable low-level gear-shifting steps according to a result of determining a state of a transmission, computing an immediate post-gear-shifting expected speed of a turbine for each step included that is within the maximum number of allowable low-level gear-shifting steps, using a current speed of an output shaft of the transmission and a gear ratio of each step and comparing the computed expected speed of the turbine with a preset allowable speed thereof for each step, setting the lowest-level gear-shifting step, among gear-shifting steps at which the expected speed of the turbine and the allowable speed thereof satisfy a predetermined condition, is set to be a target gear-shifting step, and executing gear-shifting control for shifting a current gear-shifting step down to the target gear-shifting step.

The system assigns a plurality of drivers to one or more identified routes. An onboard computing system of a vehicle collects or generates a plurality of vehicle events, which are received at a server via a network. The onboard computing system includes a plurality of sensors and subsystems that are connected to monitor various vehicle components, such as braking, steering, and radar, as well as one or more cameras. A difficulty of each of the identified routes is characterized based on the plurality of vehicle events. A performance of each driver is characterized based on a subset of the plurality of vehicle events collected or received from the vehicle of each driver.

A method for fuel efficiency optimization by predictive driving, the method comprises: determining a current state of a vehicle and current state of an environment of the vehicle; estimating a future state of the vehicle and a future state of the environment of the vehicle; wherein a future state of each one of the vehicle and the environment is a state at a future point of time following a current point of time; evaluating, whether the vehicle has to change one or more vehicle progress parameters between the current point of time and the future point of time; selecting a future driving behavior out of multiple future driving behaviors, that will implement the change of the one or more vehicle progress parameters, wherein the selecting is based on a fuel consumption associated with the change of the one or more future driving parameters; and generating at least one of a selected future driving behavior suggestion, a selected future driving behavior alert, and a selected future driving behavior command.

A method for providing a visual driving speed recommendation to an operator of a vehicle includes determining a current speed; determining a corresponding energy efficient speed; determining a higher corresponding less energy efficient speed; determining a first transition range between the corresponding energy efficient speed and the higher corresponding less energy efficient speed; and displaying, on a display, a display zone, the display zone comprising: a first indicator corresponding to the current speed; a first pattern corresponding to the corresponding energy efficient speed; a second pattern different from the first pattern and corresponding to the higher corresponding less energy efficient speed; and a first pattern range therebetween corresponding to the first transition range.

The disclosure relates generally to an in-vehicle feedback system, and more particularly, to an in-vehicle device with a display or graphical interface that collects driving data and provides feedback based on the driving data. The system may comprise an in-vehicle device that includes a graphical user interface and a processor and a data collection device wirelessly connected to the in-vehicle device. The in-vehicle device may be configured to receive vehicle telematics data from the data collection device and the processor may process the telematics data in real time and cause the telematics data to be displayed on the graphical user interface. The graphical user interface may include a speed display and an acceleration display.

A display device for displaying at least one of outputs related to traveling of a hybrid vehicle includes a first area that indicates a first output in a first mode and indicates a second output in a second mode, a second area provided side by side with the first area and that indicates a third output in the second mode, and a third area provided in a position adjacent to the second area, and that indicates a range of the first output where a possibility that the internal combustion engine starts is high. The first area has a first display portion that changes according to the first output in the first mode and the second output in the second mode. The second area has a second display portion that changes according to the third output in the second mode.

Disclosed is a vehicle fuel economy evaluation method based on data analysis. The method combines data processing and a fuel model with an enhanced learning mechanism to predict fuel consumption, analyze driving behavior and output an improvement suggestion. With continuous enhanced learning and long-term dynamic improvement, the model will be able to predict economic fuel consumption in an increasingly accurate way, along with specific and intuitive driving behavior suggestions to help drivers to drive economically.

A system and a method for predicting a battery consumption of an electric vehicle are disclosed. The battery consumption prediction system of the electric vehicle predicts the battery consumption considering an overall state of the electric vehicle and an external environment of the electric vehicle. The battery consumption prediction system of the electric vehicle may be associated with an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services, and the like.

An HV-ECU executes processing of steps. The steps include: a step of turning on a result display permission flag when an assist condition visible to a user is established; a step of calculating energy consumption in each travel section and total energy consumption when all the assist conditions are established and look-ahead information is updated; a step of generating a travel plan when the total energy consumption is greater than remaining energy; a step of executing switching control in accordance with the travel plan; and a step of outputting a result display when the result display permission flag is in an ON state when a vehicle reaches a destination.