TY - GEN
T1 - Decentralized Model Predictive Control for Automated and Connected Electric Vehicles at Signal-free Intersections
AU - Pan, Xiao
AU - Chen, Boli
AU - Dai, Li
AU - Timotheou, Stelios
AU - Evangelou, Simos A.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The development of connected and automated vehicles (CAVs) enables improvements in the safety, smoothness, and energy efficiency of the road transportation systems. This paper addresses the problem of optimally controlling battery-electric CAVs crossing an unsignalized intersection subject to a first-in-first-out crossing policy. The optimal velocity trajectory of each vehicle that minimizes the average energy consumption and travel time, is found by a decentralized model predictive control (DMPC) method via a convex modeling framework so as to ensure computational efficiency and the optimality of the solution. Numerical examples and comparisons with a centralized control counterpart demonstrate the effectiveness of the proposed decentralized coordination scheme and the trade-off between energy consumption and travel time. Further investigation into the size of the sampling interval is also provided in order to show the validity of the method in practice.
AB - The development of connected and automated vehicles (CAVs) enables improvements in the safety, smoothness, and energy efficiency of the road transportation systems. This paper addresses the problem of optimally controlling battery-electric CAVs crossing an unsignalized intersection subject to a first-in-first-out crossing policy. The optimal velocity trajectory of each vehicle that minimizes the average energy consumption and travel time, is found by a decentralized model predictive control (DMPC) method via a convex modeling framework so as to ensure computational efficiency and the optimality of the solution. Numerical examples and comparisons with a centralized control counterpart demonstrate the effectiveness of the proposed decentralized coordination scheme and the trade-off between energy consumption and travel time. Further investigation into the size of the sampling interval is also provided in order to show the validity of the method in practice.
UR - http://www.scopus.com/inward/record.url?scp=85126037464&partnerID=8YFLogxK
U2 - 10.1109/CDC45484.2021.9683495
DO - 10.1109/CDC45484.2021.9683495
M3 - Conference contribution
AN - SCOPUS:85126037464
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2659
EP - 2664
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
Y2 - 13 December 2021 through 17 December 2021
ER -
