TY - JOUR
T1 - Energy-Efficient Connected and Automated Vehicles
T2 - Real-Time Traffic Prediction-Enabled Co-Optimization of Vehicle Motion and Powertrain Operation
AU - Shao, Yunli
AU - Sun, Zongxuan
N1 - Publisher Copyright:
© 2005-2012 IEEE.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Connected and automated vehicles (CAVs) can bring energy, mobility, and safety benefits to transportation. Energy savings can be achieved by solving a mathematical optimization problem for a lookahead horizon using previewed traffic information enabled by connectivity. However, it is challenging to predict shortterm traffic, especially for mixed-traffic scenarios, where both connected and unconnected vehicles are on the road.
AB - Connected and automated vehicles (CAVs) can bring energy, mobility, and safety benefits to transportation. Energy savings can be achieved by solving a mathematical optimization problem for a lookahead horizon using previewed traffic information enabled by connectivity. However, it is challenging to predict shortterm traffic, especially for mixed-traffic scenarios, where both connected and unconnected vehicles are on the road.
UR - http://www.scopus.com/inward/record.url?scp=85112439047&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112439047&partnerID=8YFLogxK
U2 - 10.1109/mvt.2021.3085999
DO - 10.1109/mvt.2021.3085999
M3 - Article
AN - SCOPUS:85112439047
SN - 1556-6072
VL - 16
SP - 47
EP - 56
JO - IEEE Vehicular Technology Magazine
JF - IEEE Vehicular Technology Magazine
IS - 3
M1 - 9465110
ER -
