Hierarchical Economic Model Predictive Control for Eco-Driving of Electric Vehicles

This paper presents a hierarchical economic model predictive control (EMPC) framework for electric vehicles (EVs) to address traffic congestion, energy consumption, and driving safety in tracking control. The framework comprises a higher-level planner and a lower-level controller that act in tandem to achieve driving safety and economic efficiency while ensuring control performance. The higher-level planner employs an event-triggered logic by taking traffic density and economic consumption into account. The resulting optimization problem is only solved to update the reference control input of lower-level controller when the traffic density violates a pre-specified threshold. The lower-level controller ensures driving safety and control performance of EVs by designing adaptive inter-vehicle distance constraints. In addition, the robustness of the EMPC framework is ensured by adopting a robust constraint tightening policy. Recursive feasibility analyses of the optimization problems in both levels of the framework are also conducted. Rigorous proofs of asymptotic average performance and stability analysis are guaranteed for the closed-loop system. The proposed hierarchical EMPC algorithm is demonstrated to be effective and superior in a case study.