Abstract
This study develops a connected eco-driving controller for battery electric vehicles (BEVs), the BEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The developed controller can assist BEVs while traversing signalized intersections with minimal energy consumption. The calculation of the optimal vehicle trajectory is formulated as an optimization problem under the constraints of (1) vehicle acceleration/deceleration behavior, defined by a vehicle dynamics model; (2) vehicle energy consumption behavior, defined by a BEV energy consumption model; and (3) the relationship between vehicle speed, location, and signal timing, defined by vehicle characteristics and signal phase and timing (SPaT) data shared under a connected vehicle environment. The optimal speed trajectory is computed in real-time by the proposed BEV eco-CACC-I controller, so that a BEV can follow the optimal speed while negotiating a signalized intersection. The proposed BEV controller was tested in a case study to investigate its performance under various speed limits, roadway grades, and signal timings. In addition, a comparison of the optimal speed trajectories for BEVs and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions. Lastly, the proposed controller was implemented in microscopic traffic simulation software to test its networkwide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays.
Highlights
The United States is one of the world’s prime petroleum consumers, burning more than 20% of the planet’s total refined petroleum, and the surface transportation sector alone accounts for around69% of the United States’ total petroleum usage [1]
battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) was conducted to investigate the impact of vehicle engine types on eco-driving solutions
The findings in the case study prove that previous studies, which only considered the optimization of vehicle acceleration and deceleration movements and ignored the specific vehicle energy model, may not correctly compute the energy-optimal eco-driving solutions for different types of vehicles
Summary
The United States is one of the world’s prime petroleum consumers, burning more than 20% of the planet’s total refined petroleum, and the surface transportation sector alone accounts for around. A number of studies have focused on developing eco-driving algorithms to help vehicles approach signalized intersections using connected vehicle technologies These eco-driving strategies aim to provide, in real-time, recommendations to individual drivers or vehicles so that vehicle maneuvers can be appropriately adjusted to reduce fuel consumption and emission levels [7,8,9]. Many previous studies developed eco-driving strategies for ICEVs and BEVs, there is no comparison to demonstrate the differences in the energy-optimal solutions for each. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed controller can effectively reduce stop-and-go traffic in the vicinity of signalized intersections, and that the BEV Eco-CACC-I controller produces average savings of 9.3% in energy consumption and 3.9% in vehicle delays. The last section provides conclusions and recommendations for future research
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