MPC-Based Cooperative Longitudinal Control for Vehicle Strings in a Realistic Driving Environment 2023-01-0689

This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simultaneously optimizing energy efficiency. To keep our controller as close as possible to a real-life deployable technology, we also consider passenger comfort in our MPC design, which is a relevant aspect that is often a conflicting objective with respect to energy efficiency. Our simulation scenario is characterized by a homogeneous string of three battery electric vehicles and was modelled in a MATLAB/Simulink environment. An extensive set of simulation experiments forms the basis for our discussion on the energy-saving potential of cooperative driving automation systems.