A display device, for a hybrid vehicle for displaying an output relating to traveling of the hybrid vehicle, includes a first region and a second region. The first region indicates the output in a first mode in which an internal combustion engine is stopped and the vehicle travels using an electric motor. The second region indicates the output in a second mode in which the internal combustion engine is operated to travel. The first region includes a third region positioned close to the second region and indicating the output at which the internal combustion engine possibly starts up.

Excess fuel consumption monitor and feedback systems for vehicles include sensor arrays of two primary types including those sensors deployed as part of a vehicle manufacturer established sensor suite and sensors deployed as after-market sensors. Together, these sensor suites include sensors coupled to vehicle subsystems and operating environments associated with the vehicle. Data from these sensors may be used as parametric inputs to drive algorithmic calculations which have outputs that express excess fuel consumption. Expressions of excess fuel consumption may be made instantaneously as real-time feedback to a vehicle operator/driver and/or a fleet manager as part of a summary report.

The invention relates to a method for assisting a driver of a vehicle (1) having an electric drive, in which a list of predefined influencing variables for the consumption of electrical energy by the vehicle (1) is drawn up and output by an output device (14), with the influencing variables relating to factors which can be influenced by the driver of the vehicle (1), the method comprising the following steps: a) calling up characteristic maps which specify a relationship between energy consumption and the various influencing variables, b) determining possible optimizations of the energy consumption by modifying a particular influencing variable, c) computing possible energy savings on implementation of the possible optimizations of the particular influencing variable using the characteristic maps retrieved, d) sorting the influencing variables in the list.

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.

Embodiments described herein relate to the field of transport, particularly motor vehicles. A motor with predictive adjustment is described, as well as a motor controller of a vehicle, which is capable of automatically adjusting a physical parameter of a motor, such as the width of the air gap of an electric motor. A motor of a vehicle can include at least one physical parameter capable of being adjusted according to characteristic data predicted from the current path of the vehicle based on data provided by at least one vehicle motor sensor. Thus, the motor can be automatically adjusted according to characteristic data predicted from the current path based on the data of a motor sensor for optimizing the use of the motor, with respect to a parameter such as power consumption, transmission efficiency, or rotor warming, regardless of the route.

The operation of a hybrid powertrain system is optimized with respect to a desired state-of-charge trajectory, taking account of the estimated anticipated vehicle drive power. The hybrid powertrain system has an internal combustion engine and an electrically operated torque machine. The internal combustion engine and the torque machine are controlled by a control device and are connected to an output element via a hybrid transmission. Before the start of the prediction period Δt, an experience-based state-of-charge trajectory for the anticipated route, covering at least the prediction period Δt, is retrieved from an external database. The desired state-of-charge trajectory is established based on the experience-based state-of-charge trajectory by modifying it with at least one optimization constraint. The experience-based state-of-charge trajectory can be established based on operating data from hybrid powertrain systems of multiple vehicles and/or from operating data from multiple comparable journeys with the same vehicle.

The present disclosure provides a multi-stage method to extend the range of a vehicle. The method includes taking progressive actions on a vehicle as the state of charge (SOC) drops below defined levels. The method may include monitoring the SOC of the vehicle in relation to a SOC threshold or monitoring the SOC of the vehicle in relation to the distance remaining to a predetermined destination.

An apparatus comprising an interface, a memory and a processor. The interface may be configured to receive sensor data samples during operation of a vehicle. The memory may be configured to store the sensor data samples over a number of points in time. The processor may be configured to analyze the sensor data samples stored in the memory to detect a pattern. The processor may be configured to manage an application of brakes of the vehicle in response to the pattern.

A system can analyze a sensor view of a surrounding area of a self-driving vehicle (SDV). Based on analyzing the sensor view, the system can determine that a decision point along a current route of the SDV exceeds a predetermined risk threshold, and diverge the SDV from the current route based at least in part on the decision point exceeding the predetermined risk threshold.

A system and approach for a vehicle system. The vehicle system may include a vehicle, a propulsion device (e.g., a combustion engine or electric motor), and a controller. The propulsion device may at least partially power the vehicle. The controller may be in communication with the propulsion device and may control the propulsion device according to a target speed of the vehicle. The controller may include a model of energy balances of the vehicle and may use the model to estimate energy losses over a travel horizon of the vehicle. The controller may optimize a cost function over the travel horizon of the vehicle based at least in part on the estimated energy losses to set an actual speed for the vehicle. The estimated energy losses may include one or more of aerodynamic drag, vehicle friction, and conversion efficiency from the propulsion device.