A Framework for the Integrated Optimization of Charging and Power Management in Plug-in Hybrid Electric Vehicles

This paper develops a dynamic programming (DP)-based framework for simultaneously optimizing the charging and power management of a plug-in hybrid electric vehicle (PHEV). These two optimal control problems relate to activities of the PHEV on the electric grid (i.e., charging) and on the road (i.e., power management). The proposed framework solves these two problems simultaneously to avoid loss of optimality resulting from solving them separately. The framework furnishes optimal trajectories of PHEV states and control inputs over a 24-h period. We demonstrate the framework for 24-h scenarios with two driving trips and different power grid generation mixes. The results show that addressing the aforementioned optimization problems simultaneously can elucidate valuable insights. For example, for the chosen daily driving scenario, grid generation mixes, and optimization objective, it is shown that it is not always optimal to completely charge a battery before each driving trip. In addition, reduction in CO₂ resulting from the synergistic interaction of PHEVs with an electric grid containing a significant amount of wind power is studied. The main contribution of this paper to the literature is a framework that makes it possible to evaluate tradeoffs between charging and on-road power management decisions.