Optimal Control of Plug-in HEVs for Fuel Economy Under Various Travel Distances

Control of Plug-in Hybrid Electric Vehicles (PHEVs) poses a different challenge from that of the conventional Hybrid Electric Vehicle (HEV) due to the fact that the battery energy is depleted throughout the drive cycle. In particular, when the travel distance exceeds the All Electric Range (AER) of a PHEV, control of the PHEV is no longer trivial. In this paper, we develop a method for the synthesis of the supervisory powertrain controller (SPC) that achieves near-optimal performance under known travel distances. We first find the globally optimal solution using the dynamic programming (DP) technique, which serves as a benchmark of achievable performance. By analyzing the DP results, a variable Energy-to-Distance Ratio (EDR), θ, is introduced to quantify the level of battery state-of-charge (SOC) with respect to the remaining distance. This variable was found to play an important role in the energy management of PHEVs, and an adaptive SPC that adjusts engine on/off and gear-shift strategies under a wide range of θ conditions was proposed using a comprehensive extraction method. Simulation results confirm that the proposed adaptive SPC achieves near-optimal (< 1% loss) fuel economy under various EDR.