Power management for Plug-in Hybrid Electric Vehicles using Reinforcement Learning with trip information

In this paper, we present a new method of power management for Plug-in Hybrid Electric Vehicles (PHEVs) using Reinforcement Learning technique combined with trip information. Our new control strategy uses the remaining travel distance, which can be easily obtained from today´s Global Positioning System (GPS), for the energy optimization of PHEVs. For a given trip, the remaining distance is highly correlated to the future energy consumption, a quantity our controller tries to learn and optimize continuously. The simulation results confirm the self-improving capability of our reinforcement learning controller and show that our controller outperforms the rule-based controller with respect to a defined reward function.