Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems （ITS）. Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.

An enhanced eco-driving strategy based on reinforcement learning for connected electric vehicles:cooperative velocity and lane-changing control

Purpose–This study aims to propose an enhanced eco-driving strategy based on reinforcement learning(RL)to alleviate the mileage anxiety of electric vehicles(EVs)in the connected environment.Design/methodology/approach–In this paper,an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed for connected EVs.The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving.Moreover,this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.Findings–To illustrate the performance for the EEDC-HRL,the controlled EV was trained and tested in various traffic flow states.The experimental results demonstrate that the proposed technique can effectively improve energy efficiency,without sacrificing travel efficiency,comfort,safety and lane-changing performance in different traffic flow states.Originality/value–In light of the aforementioned discussion,the contributions of this paper are two-fold.An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space(EEDC-HRL)is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs.A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.