Johannes Geir Kristinsson

TL;DR: A speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information is presented.

TL;DR: In this paper, a vehicle and a method to control a vehicle includes selecting a trip route for the vehicle using a user interface, generating a charge reference profile of a battery coupled to an electric motor based on the trip, and commanding propulsion devices in the vehicle based on a location of the vehicle with respect to the trip route such that a state of charge (SOC) of the battery tracks the reference profile.

TL;DR: Experimental results show that the proposed Intelligent Trip Modeling System (ITMS) has the capability of providing accurate predictions of dynamic traffic changes and traveling speed at the beginning of a trip and can generalize well to prediction of speed profiles on the freeway routes other than the routes the system was trained on.

TL;DR: In this paper, a plugin hybrid electric vehicle includes an internal combustion engine, a battery, an electric machine, and a controller, and the controller is programmed to receive input from the driver representing a desired operating mode and a desired energy reservation.

TL;DR: In this article, a vehicle system includes a driver identification fusion module programmed to receive a confidence level from a plurality of identification modules, each confidence level indicates the likelihood that a driver of a vehicle is a particular person.