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Development of hybrid electric vehicles depends on an advanced and efficientenergy management strategy (EMS). With online and real-time requirements inmind, this article presents a human-like energy management framework for hybridelectric vehicles according to deep reinforcement learning methods andcollected historical driving data. The hybrid powertrain studied has aseries-parallel topology, and its control-oriented modeling is founded first.Then, the distinctive deep reinforcement learning (DRL) algorithm, named deepdeterministic policy gradient (DDPG), is introduced. To enhance the derivedpower split controls in the DRL framework, the global optimal controltrajectories obtained from dynamic programming (DP) are regarded as expertknowledge to train the DDPG model. This operation guarantees the optimality ofthe proposed control architecture. Moreover, the collected historical drivingdata based on experienced drivers are employed to replace the DP-basedcontrols, and thus construct the human-like EMSs. Finally, different categoriesof experiments are executed to estimate the optimality and adaptability of theproposed human-like EMS. Improvements in fuel economy and convergence rateindicate the effectiveness of the constructed control structure.

Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences