2 research outputs found
Enhanced Eco-Approach Control of Connected Electric Vehicles at Signalized Intersection with Queue Discharge Prediction
Long queues of vehicles are often found at signalized
intersections, which increases the energy consumption of all the
vehicles involved. This paper proposes an enhanced eco-approach
control (EEAC) strategy with consideration of the queue ahead for
connected electric vehicles (EVs) at a signalized intersection. The
discharge movement of the vehicle queue is predicted by an
improved queue discharge prediction method (IQDP), which takes
both vehicle and driver dynamics into account. Based on the
prediction of the queue, the EEAC strategy is designed with a
hierarchical framework: the upper-stage uses dynamic
programming to find the general trend of the energy-efficient
speed profile, which is followed by the lower-stage model
predictive controller to computes the explicit solution for a short
horizon with guaranteed safe inter-vehicular distance. Finally,
numerical simulations are conducted to demonstrate the energy
efficiency improvement of the EEAC strategy. Besides, the effects
of the queue prediction accuracy on the performance of the EEAC
strategy are also investigated
Traffic-Aware Ecological Cruising Control for Connected Electric Vehicle
The advent of intelligent connected technology has greatly enriched the capabilities of vehicles in acquiring information. The integration of short-term information from limited sensing range and long-term information from cloud-based systems in vehicle motion planning and control has become a vital means to deeply explore the energy-saving potential of vehicles. In this study, a traffic-aware ecological cruising control (T-ECC) strategy based on a hierarchical framework for connected electric vehicles in uncertain traffic environments is proposed, leveraging the two distinct temporal-dimension information. In the upper layer that is dedicated for speed planning, a sustainable energy consumption strategy (SECS) is introduced for the first time. It finds the optimal economic speed by converting variations in kinetic energy into equivalent battery energy consumption based on long-term road information. In the lower layer, a synthetic rolling-horizon optimization control (SROC) is developed to handle real-time traffic uncertainties. This control approach jointly optimizes energy efficiency, battery life, driving safety, and comfort for vehicles under dynamically changing traffic conditions. Notably, a stochastic preceding vehicle model is presented to effectively capture the uncertainties in traffic during the driving process. Finally, the proposed T-ECC is validated through simulations in both virtual and real-world driving conditions. Results demonstrate that the proposed strategy significantly improves the energy efficiency of the vehicle