Differentiated Speed Planning for Connected and Automated Electric Vehicles at Signalized Intersections considering Dynamic Wireless Power Transfer

Abstract

Deploying the dynamic wireless power transfer (DWPT) equipment at intersections can improve the transportation efficiency and decrease the energy consumption. It can easily turn the disadvantages of waiting time for red phase into the advantages of charging benefits for electric vehicles (EVs). This study develops a multiobjective speed planning model with differentiated charging strategy to optimize the EV’s trajectory at signalized intersections with DWPT. To mitigate the negative impact of DWPT, the proposed model is divided into charging priority driving (C-eco-driving) mode and time priority driving (T-eco-driving) mode for connected and automated electric vehicles (CAEVs) to apply different scenarios. Meanwhile, a widely applied car-following model, i.e., the Intelligent Driver Model (IDM), has been calibrated with the ground-truth dataset to meet the intersection characteristic for EVs in mixed traffic. The efficiency and robustness of two eco-driving modes were validated in single-vehicle and mixed traffic based on MATLAB simulation. For single vehicle, the electricity benefits of the C-eco-driving mode and T-eco-driving mode increase about 0.0867 kWh and 0.0532 kWh, respectively. T-eco-driving mode reduces 2.5 s of travel time. For mixed traffic, the C-eco-driving mode provides more charging benefits about 51~73% than no-control strategy. In contrast, the T-eco-driving mode provides significant time benefits of 2.6 s and slight electricity benefits of 12~30% than no-control strategy. The increment of market penetration rate and wireless charging length can significantly improve the charging benefits. When charging length has the same value, the closer the DWPT facilities to the intersection, the more obvious the charging benefits.

A Multi-Objective Optimal Control Method for Navigating Connected and Automated Vehicles at Signalized Intersections Based on Reinforcement Learning

“…Dynamic eco-driving on main roads can yield up to 15% fuel savings and a reduction in carbon dioxide emissions [14]. A multi-objective speed planning model optimized electric vehicle trajectories, resulting in substantial electricity and time savings [15]. Additionally, an eco-driving method based on departure time prediction reduced delays at signalized intersections by optimizing CAV trajectories, showcasing notable efficiency improvements [16].…”