Eco-Driving Adaptive Cruise Control via Model Predictive Control Enhanced with Improved Grey Wolf Optimization Algorithm

In this paper, we suggest a novel Ecological Adaptive Cruise Control (Eco-ACC) system for an autonomous electric vehicle able to drive its motion while minimizing as much as possible its energy consumption. To this aim, we consider a Nonlinear Model Predictive Control (NMPC) method enhanced with an off-line Computational-intelligence (CI)-based optimization algorithm, i,e. the Improved-Grey Wolf Optimizer (I-GWO). Specifically, since the control performances strongly depend on the proper selection of the NMPC cost function, we propose the I-GWO algorithm to help the control designer find the sub-optimal weighting factors of the dynamic cost function optimized via the NMPC. An extensive numerical analysis involving realistic vehicle dynamics and a real-life Italian road network route confirm the effectiveness of the proposed approach in guaranteeing the ACC control objectives while ensuring energy saving.