Traffic Information-Based Hierarchical Control Strategies for Eco-Driving of Plug-In Hybrid Electric Vehicles
Yapeng Li, Yalian Yang, Xianke Lin, Xiaosong Hu
Abstract
The development of intelligent transportation technology provides a great opportunity for energy efficiency improvement of electrified vehicles. However, for plug-in hybrid vehicles, eco-driving control usually involves three problems, including speed planning, SOC planning, and energy management. Solving the above three problems requires considering not only the fuel economy but also the computational efficiency. To this end, this paper proposes a hierarchical control strategy to improve driving comfort and fuel economy simultaneously for a PHEV. Specifically, three main contributions are presented to distinguish our efforts from the existing research. First, in the control framework, the traffic light information is utilized to calculate optimal driving speed by minimizing a multi-objective function. Then, the SOC planning problem is solved by convex optimization, while the fuel consumption is minimized by a predictive equivalent consumption minimization strategy. Second, the speed trajectories and fuel consumptions in the other two traffic scenarios with different traffic light SPaT (Signal Phasing and Timing) are presented to validate the effectiveness of the proposed method. Finally, the robustness with respect to prediction horizon length, initial co-state value, and gain coefficient value are analyzed and discussed.