Optimal catalyst temperature management of Plug-in Hybrid Electric Vehicles

For driving cycles that require use of the engine (i.e. the trip distance exceeds the All Electric Range (AER) of a Plug-in Hybrid Electric Vehicle (PHEV) or a driving cycle demands power exceeding the battery peak power), the catalyst temperature management for reduced tailpipe emissions is a challenging control problem due to the frequent and extended engine shut-down and catalyst cool-down. 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 dynamic programming (DP), which provides an optimal control policy and state trajectories. Based on the analysis of the optimal state trajectories, a variable Energy-to-Distance Ratio (EDR) is introduced to quantify the level of battery state-of-charge (SOC) relative to the remaining distance. A novel two-dimensional extraction method is developed to extract engine on/off, gear-shift, and power-split control strategies as functions of both EDR and the catalyst temperature from the DP control policy. 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.