Analytical greedy control and Q-learning for optimal power management of plug-in hybrid electric vehicles

摘要

In this paper, we present two solutions for achieving the optimal control of PHEVs on short trips. We prove, mathematically, that a greedy control policy is optimal for those short trips where the battery State-of-Charge (SoC) will not drop below its minimum threshold level. A closed-form greedy control solution is derived from the PHEV powertrain model. Furthermore, we provide a Q-learning based approach which has the capability of in-vehicle learning and is model-free. Our algorithm, combining the Neuro-Dynamic Programming (NDP) with estimated future trip information, can robustly converge to the optimal policy on both fixed and randomly selected drive cycles.