Hierarchical Model Predictive Control-based Electric Vehicle Fleet Charging Management

Due to relatively long parking and related connection times charging of electric vehicles (EV) is characterized by flexibility, which could be exploited for different benefits such as charging cost minimization or better utilization of intermittent renewable energy sources (RES). To this end, a predictive and optimal hierarchical charging management method of EV fleet characterized by computational efficiency and good scalability is proposed herein. The method relies on an aggregate EV fleet model, a model predictive control (MPC) which employs a dynamic programming (DP)-based algorithm for on-line optimization of an aggregate charging power, and a heuristic algorithm that distributes the aggregate charging power to individual EVs. The heuristic algorithm is set to prioritize EVs with lower state-of-energy levels and sooner time-of-departure. The proposed charging management strategy is demonstrated for the case of virtually electrified delivery vehicle fleet of a local retail company and virtual electricity production from RES, and verified against the globally optimal benchmark obtained offline by the DP algorithm.