Driving Intention Oriented Real-time Energy Management Strategy for PHEV in Urban V2X Scenario

Real-time energy management strategies has been proposed as a promising solution to reduce the fuel consumption of plug-in hybrid electric vehicles with the introduction and establishment of Internet of vehicles. In this paper, a driving intention oriented real-time energy management strategy is proposed to maximize fuel reduction performance of PHEV in urban V2X scenario. Firstly, a GRU classification model is constructed by recurrent neural network to identify the driver's driving intention from vehicle running data. Then, based on the information of transportation infrastructure and some networked vehicles, the short-term traffic speed could be predicted by shockwave method from edge computing. Finally, a real-time energy management strategy is developed to optimize the power distribution with real-time road conditions and driving intention constraints, and simulation results indicate that the energy management strategy can reasonably distribute the engine and motor output torque.