Eco-driving for Battery Electric Vehicles Using Traffic-aware Computationally Efficient Model Predictive Control

Abstract

Vehicle platooning combined with economical driving has attracted a lot of attention to alleviate range anxiety. Furthermore, model predictive control -based economical cooperative cruise controllers (eco-CACC) has shown the promises in balancing the benefits between these two technologies. The objective of this paper is to design an innovative traffic-aware and computationally efficient MPC to minimize the energy consumption of a BEV in a two-vehicle platoon in presence of dynamic traffic situation. Instead of constant inter-vehicle space constraints, adaptive in-vehicle space constraints are systematically designed for the MPC controller based on the traffic conditions in the receding prediction horizon. The proposed adaptive inter-vehicle constraints not only enable eco-driving in platooning and non-platooning regimes, but also result in low computational efforts. Simulation results show that the proposed MPC controller with adaptive inter-vehicle space constraints can achieve energy saving up to 5.25%, when compared to a baseline control with a fixed inter-vehicle distance. In addition, the proposed MPC controller with adaptive space constraint can achieve higher energy saving benefit than the MPC controllers with fixed space constraint when the computational power is limited.