Fuel Optimized Predictive Following in Low Speed Conditions
The situation when driving in dense traffic and at low
speeds is called Stop and Go. A controller for automatic
following of the car in front could under these conditions reduce
the driver's workload and keep a safety distance to the preceding
vehicle through different choices of gear and engine torque. The
aim of this thesis is to develop such a controller, with an
additional focus on lowering the fuel consumption. With help of
GPS, 3D-maps and sensors information about the slope of the road
and the preceding vehicle can be obtained. Using this information
the controller is able to predict future possible control actions
and an optimization algorithm can then find the best inputs with
respect to some criteria. The control method used is Model
Predictive Control (MPC) and as the name indicate a model of the
control object is required for the prediction. To find the
optimal sequence of inputs, the optimization method Dynamic
Programming choose the one which lead to the lowest fuel
consumption and satisfactory following. Simulations have been
made using a reference trajectory which was measured in a real
traffic jam. The simulations show that it is possible to follow
the preceding vehicle in a good way and at the same time reduce
the fuel consumption with approximately 3 %.
Johan Jonsson
2003
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
