In this study, vehicle queuing was investigated at intersections to propose an eco-driving strategy to improve vehicle energy consumption and traffic efficiency in urban traffic environments. The proposed design appro...In this study, vehicle queuing was investigated at intersections to propose an eco-driving strategy to improve vehicle energy consumption and traffic efficiency in urban traffic environments. The proposed design approach can be applied to electric vehicles, and the control framework is categorized into two layers. In the upper layer, the speed of the host vehicle is planned offline, and in the lower layer, the required control variable acceleration is determined. First, the energy optimization problem of electric vehicles passing through an intersection was constructed, and the planning vehicle speed was obtained based on the genetic algorithm(GA). Next, the speed tracking controller and distance tracking controller were designed using sliding mode control(SMC) to ensure that the vehicle can track the planning speed with safe vehicle spacing. Finally, combined with specific cases, the energy-saving effect of the proposed method in the single-vehicle scenario, and the presence of manual driving vehicles in front-and multi-vehicle driving scenarios were studied. The results revealed that the GA-based single-vehicle speed planning method reduced energy consumption by up to 16% compared with the rule-based speed planning method. Furthermore,compared with the intelligent driver model(IDM) and adaptive cruise control(ACC) methods, the GA fleet speed planning method based on V2X communication can reduce average fleet energy consumption by 26% and 24%, respectively, and improve intersection traffic efficiency. The results of the sensitivity analysis of factors affecting planned speed revealed that vehicles passing through intersections at a steady speed exhibited superior economic performance. Finally, hardware-in-the-loop(HIL)testing was performed to verify the effectiveness of the controller under real-time conditions.展开更多
基金supported by the National Natural Science Foundation of China (Grant No.52272367)。
文摘In this study, vehicle queuing was investigated at intersections to propose an eco-driving strategy to improve vehicle energy consumption and traffic efficiency in urban traffic environments. The proposed design approach can be applied to electric vehicles, and the control framework is categorized into two layers. In the upper layer, the speed of the host vehicle is planned offline, and in the lower layer, the required control variable acceleration is determined. First, the energy optimization problem of electric vehicles passing through an intersection was constructed, and the planning vehicle speed was obtained based on the genetic algorithm(GA). Next, the speed tracking controller and distance tracking controller were designed using sliding mode control(SMC) to ensure that the vehicle can track the planning speed with safe vehicle spacing. Finally, combined with specific cases, the energy-saving effect of the proposed method in the single-vehicle scenario, and the presence of manual driving vehicles in front-and multi-vehicle driving scenarios were studied. The results revealed that the GA-based single-vehicle speed planning method reduced energy consumption by up to 16% compared with the rule-based speed planning method. Furthermore,compared with the intelligent driver model(IDM) and adaptive cruise control(ACC) methods, the GA fleet speed planning method based on V2X communication can reduce average fleet energy consumption by 26% and 24%, respectively, and improve intersection traffic efficiency. The results of the sensitivity analysis of factors affecting planned speed revealed that vehicles passing through intersections at a steady speed exhibited superior economic performance. Finally, hardware-in-the-loop(HIL)testing was performed to verify the effectiveness of the controller under real-time conditions.
