Planning and control of Electric Vehicles using dynamic energy capacity models

This paper focuses on energy management for a large population of Plug-in Electric Vehicles (PEVs) for demand response applications. We consider both real time charging control as well as energy planning optimizations. The main contribution of the paper lies in the development of a novel dynamic energy capacity model in which the energy variation range of the aggregated loads available at each time step is a function of the past energy management decisions. Such a model enables systematic yet simple design of planning strategies that minimize energy costs while respecting the dynamic energy shifting capacity of the load aggregation. A further contribution is on the development of a novel stochastic hybrid system model that can fully characterizes the dynamics and stochasticity of individual charging demands for real time implementation of the planning decisions. Simulation results show that the proposed energy capacity model closely captures the capabilities of the real system. Additionally, we show how the model could be used to achieve a specific objective: minimization of daily energy costs.