Abstract The dedicated hybrid engines (DHEs) with dual-mode combustion technology can drastically reduce the fuel consumption and emissions while guarantee the power density. This paper aims to investigate the optimal control of such DHE-based plug-in hybrid electric vehicles (PHEVs) under real driving conditions, with minimum fuel penalties caused by transient engine dynamics. For this purpose, the benefits brought by artificial intelligent control and traffic preview in terms of energy efficiency can be combined with the advantages of advanced combustion engine. This paper presents a hierarchical energy management strategy (HEMS) to realize the synergy of global and instantaneous optimization. At the cloud level of HEMS, dynamic programming is applied to obtain optimal combustion mode and state of charge reference trajectories in a receding horizon. At the powertrain level, deep reinforcement learning with a ranking-prioritized experience replay algorithm is used to output optimal engine power and combustion mode for the energy management. To evaluate the proposed strategy, a dual-mode engine with homogeneous charge compression ignition and spark ignition systems is tested and mapped, with which the PHEV is modeled in GT-Suite and Matlab/Simulink. Comprehensive experiments are carried out to verify the optimality, generalization and robustness based on a standard driving cycle and a real-world driving cycle in China with GPS data recorded. The results show that the HEMS avoids frequent switching of combustion modes and outperforms the conventional methods by more than 4% and 10% in terms of fuel economy and NOx emissions, respectively, with random initial and terminal conditions.