Car-following speed collaborative energy management strategy for connected PHEV

Sequential vehicle speed planning and energy management design is widely employed in plug-in hybrid electric vehicle (PHEV). However, this hierarchical strategy is difficult to achieve comprehensive performance optimization, as vehicle speed and energy management are inherently coupled. This paper proposes a new real-time optimization car-following speed collaborative energy management strategy (RTO-SC-EMS) for connected PHEV in the context of V2X environment, Firstly, a gated recurrent unit neural network is utilized to predict the short-term speed of the lead-vehicle based on collected representative urban driving cycles incorporating traffic information. Then, the solution section of real-time optimization car-following speed collaborative energy management strategy is used to generate host-vehicle's optimal acceleration and corresponding control sequence. Finally, the effectiveness of the proposed RTO-SC-EMS was validated by Hardware-in-the-Loop (HIL) platform.