Preceding Vehicle Dynamics Modeling for Fuel Efficient Control Strategies

Abstract: Long haulage trucks are a key part of today’s goods trans-port networks. To reduce fuel costs and emissions from trucks, novel methods of regulating their speed optimally based on road slope data and other vehicles’ behavior are being developed. An important ability for these systems, when there is no vehicle to vehicle communication, is to be able to anticipate the speed of the vehicle driving in front.This master thesis explores a number of possible ap-proaches of predicting the speed of a preceding heavy ve-hicle. The work is limited to vehicles controlled by one of two common speed control systems: cruise control (CC) and look ahead cruise control (LACC) when driving on a highway. Initially, general methods of grey box and black box modeling are used. These are then refined into more specialized predictors that combine rule based algorithms with grey box or black box models.The speed controllers are found to have highly nonlinear switching behavior, making them diÿcult to predict. The general methods were found to either produce inaccurate predictions or require unacceptably large amounts of train-ing data. The two developed methods, one using switched ARX models and the other using switched grey box mod-els, required little training data and produced satisfactory results. The presented switched grey box model approach results in a 2 % reduction in fuel consumption relative to the naive assumption that the speed of the leading vehicle will remain the same over the prediction horizon.