With global warming and increased uncertainty about future vehicle fuel supply, the need for fuel efficient vehicles is growing. In this Master Thesis, design and implementation of a control strategy for fuel efficient deceleration is presented. The resulting deceleration controller, implemented in Embedded Matlab and Simulink, provides autonomous deceleration functionality to a vehicle cruise controller. Deceleration patterns are produced through online optimization of a cost function comprising fuel consumption and time loss. The cost function is calculated based on navigation information for the predicted driving path, which is also used to detect upcoming deceleration situations. The potential for fuel savings is estimated through simulation, and experiences from tentative driving tests are presented. The optimization approach proves to yield flexibility in balancing fuel savings versus time loss, while being fast enough to be run online as a prototype function in a test vehicle. The deceleration optimizer function could either be used in its current form in future cruise controllers, or as a tool for development of simplified deceleration strategies
