Improving Ride Comfort and Fuel Economy of Connected Hybrid Electric Vehicles Based on Traffic Signals and Real Road Information

Abstract

Wireless communication technology has promoted the development of connected hybrid electric vehicles (CHEVs). With traffic signal information, the fuel economy of CHEVs can be improved via optimal speed planning. However, the road environment in most existing studies is unreal and riding comfort is ignored. Therefore, this paper uses the real phase and position information of traffic lights to establish a road model and proposes a multi-objective hierarchical optimal (MOHO) strategy. First, a speed planning module is developed as the upper layer. By integrating speed constraints, slope, and traffic light information, a model predictive control (MPC)-based speed planning strategy (SPS) is developed, which improves riding comfort. Second, an energy management module is developed as the lower layer. An adaptive equivalent consumption minimization strategy (A-ECMS)-based energy management strategy (EMS) is proposed, which achieves the optimal power distribution. The results show that the proposed MOHO strategy can improve riding comfort and fuel economy while avoiding vehicle stopping at the signalized intersection under two different road conditions.