Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information

Abstract

The combination of electrification and advanced dedicated hybrid engines (DHEs) has been attached with promising prospects for the abatement of fuel consumption and emissions. To explore the potential of this solution, experimental data are used from a high-efficiency DHE operated under dual-mode combustion: the lean-burn spark-induced compression ignition (SICI) and the stoichiometric spark ignition (SI). This work endeavors to develop the integrated energy management and engine control system for such DHE-based plug-in hybrid electric vehicles (PHEVs), to this end, the synergy of artificial intelligent control and trip preview can be exploited to boost the control performance, with the engine combustion process being optimally controlled in transient. This paper presents a multistate deep reinforcement learning (M−DRL)-based energy management strategy (EMS) with a discrete–continuous hybrid action space to select optimal combustion mode while output continuous engine power demand, and its state space is expanded to incorporate the multivariate traffic and terrain information processed by a spatio-temporal data processing (STDP) framework in real time. Comprehensive experiments are carried out to validate the capability of the proposed method, based on a standard driving cycle and a real-world driving cycle in China with real-time traffic data recorded. The results indicate that the proposed M−DRL strategy avoids frequent switchover of engine operation modes as well as achieves more than 93% of the dynamic programming’s performance in terms of the fuel consumption and NOx emissions abatement.