TY - GEN
T1 - A Two-Layer Real-Time Optimal Control for Intelligent Hybrid Electric Vehicles with Connectivity
AU - Zha, Mingjun
AU - Wang, Weida
AU - Yang, Chao
AU - Li, Ying
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and autonomous vehicles are rapidly becoming one of the transformative solutions to many traffic problems. However, during the car-following process how to solve the multi-objective optimization problem including safety, economy and comfort remains a challenging task. In this paper, a two-layer control strategy for intelligent hybrid electric vehicles with connectivity is proposed to solve this problem. In the upper layer, the vehicle speed is planned based on network information to improve vehicle comfort under the premise of ensuring safety. In the lower layer, based on the velocity trajectory obtained from the upper layer, the alternating direction method of multipliers (ADMM) algorithm is used to allocate the engine and motor torque to improve the fuel economy. Simulation results show that following safety can be guaranteed. And proposed energy management strategy (EMS) can obtain 6.96% fuel economy improvement compared with rule-based EMS under the China typical urban driving cycle.
AB - Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and autonomous vehicles are rapidly becoming one of the transformative solutions to many traffic problems. However, during the car-following process how to solve the multi-objective optimization problem including safety, economy and comfort remains a challenging task. In this paper, a two-layer control strategy for intelligent hybrid electric vehicles with connectivity is proposed to solve this problem. In the upper layer, the vehicle speed is planned based on network information to improve vehicle comfort under the premise of ensuring safety. In the lower layer, based on the velocity trajectory obtained from the upper layer, the alternating direction method of multipliers (ADMM) algorithm is used to allocate the engine and motor torque to improve the fuel economy. Simulation results show that following safety can be guaranteed. And proposed energy management strategy (EMS) can obtain 6.96% fuel economy improvement compared with rule-based EMS under the China typical urban driving cycle.
UR - http://www.scopus.com/inward/record.url?scp=85141863575&partnerID=8YFLogxK
U2 - 10.1109/ITSC55140.2022.9922281
DO - 10.1109/ITSC55140.2022.9922281
M3 - Conference contribution
AN - SCOPUS:85141863575
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2075
EP - 2079
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Y2 - 8 October 2022 through 12 October 2022
ER -