Learning-based Eco-driving Strategy Design for Connected Power-split Hybrid Electric Vehicles at signalized corridors

Abstract

The eco-driving strategy that targets driving speed optimization is recognized as a promising technique to improve vehicle energy efficiency. However, it is difficult to achieve real-time eco-driving control of hybrid electric vehicle (HEV) since the speed optimization and powertrain energy management should be resolved simultaneously. This paper proposes a hierarchical control architecture consisting of learning-based velocity planner and real-time energy management system. In the upper stage, Proximal Policy optimization (PPO) agent is trained to generate acceleration which meets multiple control objectives. The lower stage adopts Equivalent Consumption Minimization Strategy (ECMS) for real-time power split control considering powertrain dynamics. Finally, the eco-driving simulations of six signalized intersections in Nanjing are conducted. Compared with two different rule-based strategies, the proposed control architecture can achieve at least 7.39% of fuel economy saving and avoid a significant drop in the battery state of charge at the expense of higher than 5% of travel time. Simulation results also prove that the proposed strategy has an energy-saving potential in unseen scenarios.