A Novel Economic Model Predictive Control for Adaptive Cruise Control in Electric Vehicles

Abstract Enhancing the operational energy efficiency of electric vehicles (EVs) is a critical determinant in reducing their overall environmental footprint. This concern is still growing in the recent years, which have been characterized by an escalating environmental impact of global warming and pollution. In this context, autonomous vehicle functions, such as Adaptive Cruise Control (ACC), typically overlook economic criteria like energy optimization and electric powertrain dynamics, as reconciling tracking and energy-efficient control tasks remains a nontrivial challenge. In this paper, we leverage the Economic Model Predictive Control (E-MPC) as a promising control strategy to design the ACC driving assistance system in EVs. The proposed approach enhances the economic efficiency of the electric powertrain while simultaneously balancing the economic performance with the satisfaction of safe inter-vehicular spacing and smooth acceleration dynamics. With this objects, the proposed E-MPC formulation provides an optimal control policy that averages the trade-off between tracking performance and electric motor efficiency. The effectiveness of this approach is thoroughly assessed through simulations, where a platoon of vehicles – each one equipped with an E-MPC-based ACC – is asked to coherently keep its string structure, while following the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) driving cycle. Simulation results proficiently demonstrate the effectiveness of the proposed E-MPC strategy, which significantly extends EV driving range and improves sustainability without compromising tracking performance and ensuring the string stability.