This master thesis proposes methods for improving fuel economy and emissions of vehicles via use of future information of state of traffic lights, traffic flow, and deterministic traffic flow models. The first part of this thesis proposes use of upcoming traffic signal information within the vehicle\u27s adaptive cruise control system to reduce idle time at stop lights and lower fuel use. To achieve this goal an optimization-based control algorithm is formulated for each equipped vehicle that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle. The objectives are timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed. Three example simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time. The second part of this thesis addresses the use of traffic flow information to derive the fuel- or time-optimal velocity trajectory. A vehicle\u27s untimely arrival at a local traffic wave with lots of stops and goes increases its fuel use. This paper proposes predictive planning of the vehicle velocity for reducing the velocity transients in upcoming traffic waves. In this part of the thesis macroscopic evolution of traffic pattern along the vehicle route is first estimated by combining a traffic flow model and real-time traffic data streams. The fuel optimal velocity trajectory is calculated by solving an optimal control problem with the spatiotemporally varying constraint imposed by the traffic. Simulation results indicatethe potential for considerable improvements in fuel economy with a little compromise on travel time
