Control of plug-in hybrid electric vehicles (PHEVs) poses a different challenge from that of the conventional hybrid electric vehicle (HEV) because the battery energy is designed to deplete throughout the drive cycle. In particular, when the travel distance exceeds the all-electric range (AER) of a PHEV and when tailpipe emissions are considered, optimal operation of the PHEV must consider optimization of the performance over a time horizon. In this paper, we develop a method to synthesize a supervisory powertrain controller (SPC) that achieves near-optimal fuel economy and tailpipe emissions under known travel distances. We first find the globally optimal solution using the dynamic programming (DP) technique, which provides an optimal control policy and state trajectories. Based on the analysis of the optimal state trajectories, a new variable energy-to-distance ratio (EDR), theta, is introduced to quantify the level of battery state-of-charge (SOC) relative to the remaining distance. This variable plays an important role in adjusting both energy and catalyst thermal management strategies for PHEVs. A novel extraction method is developed to extract adjustable engine on/off, gear-shift, and power-split strategies from the DP control policy over the entire state space. Based on the extracted results, an adaptive SPC that optimally adjusts the engine on/off, gear-shift, and power-split strategies under various EDR and catalyst temperature conditions was developed to achieve near-optimal fuel economy and emission performance.